From 5c254697375d599a5a32530ec91cac58df8b53a7 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 7 Oct 2024 04:28:03 +0000 Subject: [PATCH 01/39] Added draw triangle with success condition --- mani_skill/envs/tasks/drawing/__init__.py | 1 + .../envs/tasks/drawing/draw_triangle.py | 301 ++++++++++++++++++ .../motionplanning/panda_stick/run.py | 92 ++++++ .../panda_stick/solutions/__init__.py | 1 + .../panda_stick/solutions/draw_triangle.py | 41 +++ 5 files changed, 436 insertions(+) create mode 100644 mani_skill/envs/tasks/drawing/draw_triangle.py create mode 100644 mani_skill/examples/motionplanning/panda_stick/run.py create mode 100644 mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py create mode 100644 mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py diff --git a/mani_skill/envs/tasks/drawing/__init__.py b/mani_skill/envs/tasks/drawing/__init__.py index c6df1f738..f90b9a236 100644 --- a/mani_skill/envs/tasks/drawing/__init__.py +++ b/mani_skill/envs/tasks/drawing/__init__.py @@ -1 +1,2 @@ from .draw import * +from .draw_triangle import * \ No newline at end of file diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py new file mode 100644 index 000000000..8275c90b0 --- /dev/null +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -0,0 +1,301 @@ +import random +from typing import Dict + +import numpy as np +import sapien +import torch +from transforms3d.euler import euler2quat + +from mani_skill.agents.robots.panda.panda_stick import PandaStick +from mani_skill.envs.sapien_env import BaseEnv +from mani_skill.sensors.camera import CameraConfig +from mani_skill.utils import sapien_utils +from mani_skill.utils.registration import register_env +from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder +from mani_skill.utils.structs.actor import Actor +from mani_skill.utils.structs.pose import Pose +from mani_skill.utils.structs.types import SceneConfig, SimConfig +import math + +@register_env("DrawTriangle-v1", max_episode_steps=1000) +class DrawTriangle(BaseEnv): + """ + This is a simple environment demonstrating drawing simulation on a table with a robot arm. There are no success/rewards defined, users can use this code as a starting point + to create their own drawing type tasks. + """ + + MAX_DOTS = 1010 + """ + The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering + when too many objects are being rendered in many scenes. + """ + DOT_THICKNESS = 0.003 + """thickness of the paint drawn on to the canvas""" + CANVAS_THICKNESS = 0.02 + """How thick the canvas on the table is""" + BRUSH_RADIUS = 0.01 + """The brushes radius""" + BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]] + """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" + THRESHOLD = 0.1 + + + SUPPORTED_REWARD_MODES = ["none"] + + SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore + agent: PandaStick + + def __init__(self, *args, robot_uids="panda_stick", **kwargs): + super().__init__(*args, robot_uids=robot_uids, **kwargs) + + @property + def _default_sim_config(self): + # we set contact_offset to a small value as we are not expecting to make any contacts really apart from the brush hitting the canvas too hard. + # We set solver iterations very low as this environment is not doing a ton of manipulation (the brush is attached to the robot after all) + return SimConfig( + sim_freq=100, + control_freq=20, + scene_config=SceneConfig( + contact_offset=0.01, + solver_position_iterations=4, + solver_velocity_iterations=0, + ), + ) + + @property + def _default_sensor_configs(self): + pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1]) + return [ + CameraConfig( + "base_camera", + pose=pose, + width=320, + height=240, + fov=1.2, + near=0.01, + far=100, + ) + ] + + @property + def _default_human_render_camera_configs(self): + pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1]) + return CameraConfig( + "render_camera", + pose=pose, + width=1280, + height=960, + fov=1.2, + near=0.01, + far=100, + ) + + def _load_scene(self, options: dict): + + self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0) + self.table_scene.build() + def create_goal_triangle(name="tri", base_color=None): + + box1_half_w = 0.3 / 2 + box1_half_h = 0.01 / 2 + half_thickness = 0.04 / 2 + + # change, rn too lazy to import pi correctly + pi = 3.1415926 + + # approx div by sqrt(3) + radius = (box1_half_w) / 1.73205080757 + + theta = random.random() * (2/3 * pi) + + # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation + c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01]) + c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01]) + c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01]) + self.v1 = (c1 + c3) - c2 + self.v2 = (c1 + c2) - c3 + self.v3 = (c2 + c3) - c1 + + builder = self.scene.create_actor_builder() + first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2))) + first_block_size = [box1_half_w, box1_half_h, half_thickness] + builder.add_box_visual( + pose=first_block_pose, + half_size=first_block_size, + material=sapien.render.RenderMaterial( + base_color=base_color, + ), + ) + + second_block_pose = sapien.Pose(list(c2), euler2quat(0,0,theta-(5*pi/6))) + second_block_size = [box1_half_w, box1_half_h, half_thickness] + # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) + builder.add_box_visual( + pose=second_block_pose, + half_size=second_block_size, + material=sapien.render.RenderMaterial( + base_color=base_color, + ), + ) + + third_block_pose = sapien.Pose(list(c3),euler2quat(0,0,theta -(pi/6))) + third_block_size = [box1_half_w, box1_half_h, half_thickness] + # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) + builder.add_box_visual( + pose=third_block_pose, + half_size=third_block_size, + material=sapien.render.RenderMaterial( + base_color=base_color, + ), + ) + return builder.build_kinematic(name=name) + + + # build a white canvas on the table + self.canvas = self.scene.create_actor_builder() + self.canvas.add_box_visual( + half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2], + material=sapien.render.RenderMaterial(base_color=[1, 1, 1, 1]), + ) + self.canvas.add_box_collision( + half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2], + ) + self.canvas.initial_pose = sapien.Pose(p=[-0.1, 0, self.CANVAS_THICKNESS / 2]) + self.canvas = self.canvas.build_static(name="canvas") + + self.dots = [] + self.dot_pos = [] + color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,)) + for i in range(self.MAX_DOTS): + actors = [] + if len(self.BRUSH_COLORS) > 1: + for env_idx in range(self.num_envs): + builder = self.scene.create_actor_builder() + builder.add_cylinder_visual( + radius=self.BRUSH_RADIUS, + half_length=self.DOT_THICKNESS / 2, + material=sapien.render.RenderMaterial( + base_color=self.BRUSH_COLORS[color_choices[env_idx]] + ), + ) + builder.set_scene_idxs([env_idx]) + actor = builder.build_kinematic(name=f"dot_{i}_{env_idx}") + actors.append(actor) + self.dots.append(Actor.merge(actors)) + else: + builder = self.scene.create_actor_builder() + builder.add_cylinder_visual( + radius=self.BRUSH_RADIUS, + half_length=self.DOT_THICKNESS / 2, + material=sapien.render.RenderMaterial( + base_color=self.BRUSH_COLORS[0] + ), + ) + actor = builder.build_kinematic(name=f"dot_{i}") + self.dots.append(actor) + self.goal_tee = create_goal_triangle( + name="goal_tri", + base_color=np.array([255, 255, 255, 255/4]) / 255, + ) + + def _initialize_episode(self, env_idx: torch.Tensor, options: dict): + # NOTE (stao): for simplicity this task cannot handle partial resets + self.draw_step = 0 + with torch.device(self.device): + self.table_scene.initialize(env_idx) + for dot in self.dots: + # initially spawn dots in the table so they aren't seen + dot.set_pose( + sapien.Pose( + p=[0, 0, -self.DOT_THICKNESS], q=euler2quat(0, np.pi / 2, 0) + ) + ) + + def _after_control_step(self): + if self.gpu_sim_enabled: + self.scene._gpu_fetch_all() + + # This is the actual, GPU parallelized, drawing code. + # This is not real drawing but seeks to mimic drawing by placing dots on the canvas whenever the robot is close enough to the canvas surface + # We do not actually check if the robot contacts the table (although that is possible) and instead use a fast method to check. + # We add a 0.005 meter of leeway to make it easier for the robot to get close to the canvas and start drawing instead of having to be super close to the table. + robot_touching_table = ( + self.agent.tcp.pose.p[:, 2] + < self.CANVAS_THICKNESS + self.DOT_THICKNESS + 0.005 + ) + robot_brush_pos = torch.zeros((self.num_envs, 3), device=self.device) + robot_brush_pos[:, 2] = -self.DOT_THICKNESS + robot_brush_pos[robot_touching_table, :2] = self.agent.tcp.pose.p[ + robot_touching_table, :2 + ] + robot_brush_pos[robot_touching_table, 2] = ( + self.DOT_THICKNESS / 2 + self.CANVAS_THICKNESS + ) + # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible + new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) + self.dots[self.draw_step].set_pose( + new_dot_pos + ) + if new_dot_pos.get_p()[:,-1] > 0: + self.dot_pos.append(new_dot_pos.get_p()) + + self.draw_step += 1 + + # on GPU sim we have to call _gpu_apply_all() to apply the changes we make to object poses. + if self.gpu_sim_enabled: + self.scene._gpu_apply_all() + + def evaluate(self): + out = self.success_check() + return { "success" : out} + + def _get_obs_extra(self, info: Dict): + return dict( + tcp_pose=self.agent.tcp.pose.raw_pose, + ) + + def generate_triangle_with_points(self, n, vertices, interpolate = 0): + # Create an empty array to hold the points, starting with the vertices + all_points = [] + + # Loop through each side of the triangle and generate `n` points on each side + for i in range(len(vertices)): + start_vertex = vertices[i] + end_vertex = vertices[(i+1) % len(vertices)] + + # Generate n+1 points between start and end vertex, including the start point but excluding the end point + intermediate_points = np.linspace(start_vertex, end_vertex, n+2)[:-1] + + # Add the points to the all_points list + all_points.extend(intermediate_points) + + # Convert the list of points to a numpy array + all_points = np.array(all_points) + + for i in range(interpolate): + ind = i = np.random.randint(0, all_points.shape[0] - 1) + p1 = all_points[ind] + p2 = all_points[ind + 1] + p_interpolated = (0.5) * p1 + 0.5 * p2 + all_points = np.insert(all_points, ind + 1, p_interpolated, axis=0) + + + return all_points + + def success_check(self): + if len(self.dot_pos) == 0: + return torch.Tensor([False]).to(bool) + d_points = [] + for pos in self.dot_pos: + d_points.append(pos.cpu().numpy()[:,:-1]) + + drawn_pts = np.vstack(d_points) + + target_points = self.generate_triangle_with_points(100, [self.v1[:-1], self.v2[:-1], self.v3[:-1]],0) + distance_matrix = np.sqrt(np.sum((drawn_pts[:, None, :] - target_points[None, :, :]) ** 2, axis=-1)) + Y_closeness = np.min(distance_matrix, axis=0) < self.THRESHOLD + + return torch.Tensor([np.all(Y_closeness)]).to(bool) + + + diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py new file mode 100644 index 000000000..5066fbc38 --- /dev/null +++ b/mani_skill/examples/motionplanning/panda_stick/run.py @@ -0,0 +1,92 @@ +import argparse +import gymnasium as gym +import numpy as np +from tqdm import tqdm +import os.path as osp +from mani_skill.utils.wrappers.record import RecordEpisode +from mani_skill.examples.motionplanning.panda_stick.solutions import solveDrawTriangle + +MP_SOLUTIONS = { + "DrawTriangle-v1" : solveDrawTriangle +} +def parse_args(args=None): + parser = argparse.ArgumentParser() + parser.add_argument("-e", "--env-id", type=str, default="DrawTriangle-v1", help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}") + parser.add_argument("-o", "--obs-mode", type=str, default="none", help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.") + parser.add_argument("-n", "--num-traj", type=int, default=10, help="Number of trajectories to generate.") + parser.add_argument("--only-count-success", action="store_true", help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos") + parser.add_argument("--reward-mode", type=str) + parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'") + parser.add_argument("--render-mode", type=str, default="rgb_array", help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos") + parser.add_argument("--vis", action="store_true", help="whether or not to open a GUI to visualize the solution live") + parser.add_argument("--save-video", action="store_true", help="whether or not to save videos locally") + parser.add_argument("--traj-name", type=str, help="The name of the trajectory .h5 file that will be created.") + parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer") + parser.add_argument("--record-dir", type=str, default="demos", help="where to save the recorded trajectories") + return parser.parse_args() + +def main(args): + env_id = args.env_id + env = gym.make( + env_id, + obs_mode=args.obs_mode, + control_mode="pd_joint_pos", + render_mode="rgb_array", + ) + if env_id not in MP_SOLUTIONS: + raise RuntimeError(f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}") + env = RecordEpisode( + env, + output_dir=osp.join(args.record_dir, env_id, "motionplanning"), + trajectory_name=args.traj_name, save_video=args.save_video, + source_type="motionplanning", + source_desc="official motion planning solution from ManiSkill contributors", + video_fps=30, + save_on_reset=False + ) + solve = MP_SOLUTIONS[env_id] + print(f"Motion Planning Running on {env_id}") + pbar = tqdm(range(args.num_traj)) + seed = 0 + successes = [] + solution_episode_lengths = [] + failed_motion_plans = 0 + passed = 0 + while True: + res = solve(env, seed=seed, debug=False, vis=True if args.vis else False) + if res == -1: + success = False + failed_motion_plans += 1 + else: + success = res[-1]["success"].item() + elapsed_steps = res[-1]["elapsed_steps"].item() + solution_episode_lengths.append(elapsed_steps) + successes.append(success) + if args.only_count_success and not success: + seed += 1 + env.flush_trajectory(save=False) + if args.save_video: + env.flush_video(save=False) + continue + else: + env.flush_trajectory() + if args.save_video: + env.flush_video() + pbar.update(1) + pbar.set_postfix( + dict( + success_rate=np.mean(successes), + failed_motion_plan_rate=failed_motion_plans / (seed + 1), + avg_episode_length=np.mean(solution_episode_lengths), + max_episode_length=np.max(solution_episode_lengths), + # min_episode_length=np.min(solution_episode_lengths) + ) + ) + seed += 1 + passed += 1 + if passed == args.num_traj: + break + env.close() + +if __name__ == "__main__": + main(parse_args()) \ No newline at end of file diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py new file mode 100644 index 000000000..8186dd53f --- /dev/null +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py @@ -0,0 +1 @@ +from .draw_triangle import solve as solveDrawTriangle \ No newline at end of file diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py new file mode 100644 index 000000000..1a3086e72 --- /dev/null +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py @@ -0,0 +1,41 @@ +import numpy as np +import sapien +from PIL import Image + +from mani_skill.envs.tasks import PushCubeEnv +from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ + PandaStickMotionPlanningSolver + +def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): + env.reset(seed=seed) + planner = PandaStickMotionPlanningSolver( + env, + debug=debug, + vis=vis, + base_pose=env.unwrapped.agent.robot.pose, + visualize_target_grasp_pose=vis, + print_env_info=False, + ) + + FINGER_LENGTH = 0.025 + env = env.unwrapped + + + rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) + reach_pose = sapien.Pose(p=list(env.v1), q = rot) + res = planner.move_to_pose_with_screw(reach_pose) + # -------------------------------------------------------------------------- # + # Move to second vertex + # -------------------------------------------------------------------------- # + + reach_pose = sapien.Pose(p=list(env.v2), q = rot) + res = planner.move_to_pose_with_screw(reach_pose) + + reach_pose = sapien.Pose(p=list(env.v3), q = rot) + res = planner.move_to_pose_with_screw(reach_pose) + + reach_pose = sapien.Pose(p=list(env.v1), q = rot) + res = planner.move_to_pose_with_screw(reach_pose) + + planner.close() + return res \ No newline at end of file From f1e4826255329941211d19326f6d85cbb14ef6b8 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 10 Oct 2024 01:08:05 +0000 Subject: [PATCH 02/39] parallelized progress --- .../envs/tasks/drawing/draw_triangle.py | 89 ++++++++++--------- .../panda_stick/solutions/draw_triangle.py | 8 +- 2 files changed, 50 insertions(+), 47 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 8275c90b0..26ad7cda8 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -5,7 +5,10 @@ import sapien import torch from transforms3d.euler import euler2quat +from transforms3d.quaternions import quat2mat +import mani_skill.envs.utils.randomization as randomization +from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix from mani_skill.agents.robots.panda.panda_stick import PandaStick from mani_skill.envs.sapien_env import BaseEnv from mani_skill.sensors.camera import CameraConfig @@ -106,15 +109,13 @@ def create_goal_triangle(name="tri", base_color=None): # approx div by sqrt(3) radius = (box1_half_w) / 1.73205080757 - theta = random.random() * (2/3 * pi) + theta = pi/2 # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01]) c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01]) c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01]) - self.v1 = (c1 + c3) - c2 - self.v2 = (c1 + c2) - c3 - self.v3 = (c2 + c3) - c1 + self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1]) builder = self.scene.create_actor_builder() first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2))) @@ -164,7 +165,7 @@ def create_goal_triangle(name="tri", base_color=None): self.canvas = self.canvas.build_static(name="canvas") self.dots = [] - self.dot_pos = [] + self.dot_pos = None color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,)) for i in range(self.MAX_DOTS): actors = [] @@ -193,7 +194,7 @@ def create_goal_triangle(name="tri", base_color=None): ) actor = builder.build_kinematic(name=f"dot_{i}") self.dots.append(actor) - self.goal_tee = create_goal_triangle( + self.goal_tri = create_goal_triangle( name="goal_tri", base_color=np.array([255, 255, 255, 255/4]) / 255, ) @@ -202,7 +203,22 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): # NOTE (stao): for simplicity this task cannot handle partial resets self.draw_step = 0 with torch.device(self.device): + b = len(env_idx) self.table_scene.initialize(env_idx) + target_pos = torch.zeros((b, 3)) + + target_pos[:,:2] = torch.rand((b,2))*0.02-0.1 + target_pos[:,-1] = 0.01 + qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) + mats = quaternion_to_matrix(qs) + self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1))) # b, 3, 3 + self.vertices = torch.matmul(self.vertices.double(), mats.double()) + self.vertices += target_pos + + + self.triangles = self.generate_triangle_with_points(100, self.vertices[:,:,:-1]) + for dot in self.dots: # initially spawn dots in the table so they aren't seen dot.set_pose( @@ -237,7 +253,9 @@ def _after_control_step(self): new_dot_pos ) if new_dot_pos.get_p()[:,-1] > 0: - self.dot_pos.append(new_dot_pos.get_p()) + if self.dot_pos == None: + self.dot_pos = new_dot_pos.get_p()[:, None, :] + self.dot_pos = torch.cat((self.dot_pos, new_dot_pos.get_p()[:, None,: ]), dim=1) self.draw_step += 1 @@ -254,48 +272,33 @@ def _get_obs_extra(self, info: Dict): tcp_pose=self.agent.tcp.pose.raw_pose, ) - def generate_triangle_with_points(self, n, vertices, interpolate = 0): - # Create an empty array to hold the points, starting with the vertices + def generate_triangle_with_points(self, n, vertices): + batch_size = vertices.shape[0] + all_points = [] - # Loop through each side of the triangle and generate `n` points on each side - for i in range(len(vertices)): - start_vertex = vertices[i] - end_vertex = vertices[(i+1) % len(vertices)] - - # Generate n+1 points between start and end vertex, including the start point but excluding the end point - intermediate_points = np.linspace(start_vertex, end_vertex, n+2)[:-1] - - # Add the points to the all_points list - all_points.extend(intermediate_points) - - # Convert the list of points to a numpy array - all_points = np.array(all_points) - - for i in range(interpolate): - ind = i = np.random.randint(0, all_points.shape[0] - 1) - p1 = all_points[ind] - p2 = all_points[ind + 1] - p_interpolated = (0.5) * p1 + 0.5 * p2 - all_points = np.insert(all_points, ind + 1, p_interpolated, axis=0) - + for i in range(vertices.shape[1]): + start_vertex = vertices[:, i, :] + end_vertex = vertices[:, (i+1) % vertices.shape[1], :] + t = torch.linspace(0, 1, n+2, device=vertices.device)[:-1] + t = t.view(1, -1, 1).repeat(batch_size, 1, 2) + intermediate_points = start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t + all_points.append(intermediate_points) + all_points = torch.cat(all_points, dim=1) return all_points def success_check(self): - if len(self.dot_pos) == 0: + if self.dot_pos == None or len(self.dot_pos) == 0: return torch.Tensor([False]).to(bool) - d_points = [] - for pos in self.dot_pos: - d_points.append(pos.cpu().numpy()[:,:-1]) - - drawn_pts = np.vstack(d_points) - - target_points = self.generate_triangle_with_points(100, [self.v1[:-1], self.v2[:-1], self.v3[:-1]],0) - distance_matrix = np.sqrt(np.sum((drawn_pts[:, None, :] - target_points[None, :, :]) ** 2, axis=-1)) - Y_closeness = np.min(distance_matrix, axis=0) < self.THRESHOLD - - return torch.Tensor([np.all(Y_closeness)]).to(bool) - + drawn_pts = self.dot_pos[:,:,:-1] + distance_matrix = torch.sqrt( + torch.sum( + (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, + axis=-1 + ) + ) + Y_closeness = torch.min(distance_matrix, dim = 1).values < self.THRESHOLD + return torch.Tensor([torch.all(Y_closeness)]).to(bool) diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py index 1a3086e72..29d7c5d9a 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py @@ -22,19 +22,19 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) - reach_pose = sapien.Pose(p=list(env.v1), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0,0].numpy()), q = rot) res = planner.move_to_pose_with_screw(reach_pose) # -------------------------------------------------------------------------- # # Move to second vertex # -------------------------------------------------------------------------- # - reach_pose = sapien.Pose(p=list(env.v2), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0,1]), q = rot) res = planner.move_to_pose_with_screw(reach_pose) - reach_pose = sapien.Pose(p=list(env.v3), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0,2]), q = rot) res = planner.move_to_pose_with_screw(reach_pose) - reach_pose = sapien.Pose(p=list(env.v1), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0,0]), q = rot) res = planner.move_to_pose_with_screw(reach_pose) planner.close() From de9b921b471c72c8edd39b8aebb419cf8edb8bda Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 10 Oct 2024 06:16:22 +0000 Subject: [PATCH 03/39] fixed triangle rotation issues --- .../envs/tasks/drawing/draw_triangle.py | 19 ++++++++++--------- mani_skill/examples/.gitignore | 3 ++- 2 files changed, 12 insertions(+), 10 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 26ad7cda8..41ad88508 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -4,7 +4,7 @@ import numpy as np import sapien import torch -from transforms3d.euler import euler2quat +from transforms3d.euler import euler2quat, quat2euler from transforms3d.quaternions import quat2mat import mani_skill.envs.utils.randomization as randomization @@ -40,7 +40,7 @@ class DrawTriangle(BaseEnv): """The brushes radius""" BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]] """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" - THRESHOLD = 0.1 + THRESHOLD = 0.05 SUPPORTED_REWARD_MODES = ["none"] @@ -112,9 +112,9 @@ def create_goal_triangle(name="tri", base_color=None): theta = pi/2 # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation - c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.01]) - c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.01]) - c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.01]) + c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.001]) + c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.001]) + c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.001]) self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1]) builder = self.scene.create_actor_builder() @@ -196,7 +196,7 @@ def create_goal_triangle(name="tri", base_color=None): self.dots.append(actor) self.goal_tri = create_goal_triangle( name="goal_tri", - base_color=np.array([255, 255, 255, 255/4]) / 255, + base_color=np.array([50, 50, 50, 200]) / 255, ) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): @@ -210,10 +210,11 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): target_pos[:,:2] = torch.rand((b,2))*0.02-0.1 target_pos[:,-1] = 0.01 qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) - mats = quaternion_to_matrix(qs) - self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + mats = quaternion_to_matrix(qs) + self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q= qs)) + self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1))) # b, 3, 3 - self.vertices = torch.matmul(self.vertices.double(), mats.double()) + self.vertices = (mats.double() @ self.vertices.transpose(-1,-2).double()).transpose(-1,-2) # apply rotation matrix self.vertices += target_pos diff --git a/mani_skill/examples/.gitignore b/mani_skill/examples/.gitignore index f3e6bd028..d29c740d4 100644 --- a/mani_skill/examples/.gitignore +++ b/mani_skill/examples/.gitignore @@ -1 +1,2 @@ -videos/ \ No newline at end of file +videos/ +demos/ \ No newline at end of file From 1189e323c542c711afac4a1ce873beee56f8114c Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 14 Oct 2024 19:34:36 +0000 Subject: [PATCH 04/39] clean up and format --- .../envs/tasks/drawing/draw_triangle.py | 136 ++++++++++-------- .../motionplanning/panda_stick/run.py | 104 +++++++++++--- .../panda_stick/solutions/__init__.py | 2 +- .../panda_stick/solutions/draw_triangle.py | 17 ++- 4 files changed, 172 insertions(+), 87 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 41ad88508..5c8c39351 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -1,24 +1,25 @@ +import math import random from typing import Dict +import mani_skill.envs.utils.randomization as randomization import numpy as np import sapien import torch -from transforms3d.euler import euler2quat, quat2euler -from transforms3d.quaternions import quat2mat - -import mani_skill.envs.utils.randomization as randomization -from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix from mani_skill.agents.robots.panda.panda_stick import PandaStick from mani_skill.envs.sapien_env import BaseEnv from mani_skill.sensors.camera import CameraConfig from mani_skill.utils import sapien_utils +from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix from mani_skill.utils.registration import register_env -from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder +from mani_skill.utils.scene_builder.table.scene_builder import \ + TableSceneBuilder from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig -import math +from transforms3d.euler import euler2quat, quat2euler +from transforms3d.quaternions import quat2mat + @register_env("DrawTriangle-v1", max_episode_steps=1000) class DrawTriangle(BaseEnv): @@ -42,10 +43,9 @@ class DrawTriangle(BaseEnv): """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" THRESHOLD = 0.05 - SUPPORTED_REWARD_MODES = ["none"] - SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore + SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore agent: PandaStick def __init__(self, *args, robot_uids="panda_stick", **kwargs): @@ -94,31 +94,44 @@ def _default_human_render_camera_configs(self): ) def _load_scene(self, options: dict): - + self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0) self.table_scene.build() + def create_goal_triangle(name="tri", base_color=None): - + box1_half_w = 0.3 / 2 box1_half_h = 0.01 / 2 - half_thickness = 0.04 / 2 + half_thickness = 0.04 / 2 - # change, rn too lazy to import pi correctly - pi = 3.1415926 + radius = (box1_half_w) / math.sqrt(3) - # approx div by sqrt(3) - radius = (box1_half_w) / 1.73205080757 - - theta = pi/2 + theta = np.pi / 2 # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation - c1 = np.array([radius* math.cos(theta),radius* math.sin(theta), 0.001]) - c2 = np.array([radius * math.cos(theta + (2* pi/3)), radius * math.sin(theta + (2* pi /3)), 0.001]) - c3 = np.array([radius * math.cos((theta+ (4*pi/3))), radius * math.sin(theta + (4*pi/3)), 0.001]) - self.original_verts = np.array([(c1 + c3) - c2 , (c1 + c2) - c3, (c2 + c3) - c1]) + c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.001]) + c2 = np.array( + [ + radius * math.cos(theta + (2 * np.pi / 3)), + radius * math.sin(theta + (2 * np.pi / 3)), + 0.001, + ] + ) + c3 = np.array( + [ + radius * math.cos((theta + (4 * np.pi / 3))), + radius * math.sin(theta + (4 * np.pi / 3)), + 0.001, + ] + ) + self.original_verts = np.array( + [(c1 + c3) - c2, (c1 + c2) - c3, (c2 + c3) - c1] + ) builder = self.scene.create_actor_builder() - first_block_pose = sapien.Pose(list(c1), euler2quat(0,0,theta - (pi/2))) + first_block_pose = sapien.Pose( + list(c1), euler2quat(0, 0, theta - (np.pi / 2)) + ) first_block_size = [box1_half_w, box1_half_h, half_thickness] builder.add_box_visual( pose=first_block_pose, @@ -127,10 +140,12 @@ def create_goal_triangle(name="tri", base_color=None): base_color=base_color, ), ) - - second_block_pose = sapien.Pose(list(c2), euler2quat(0,0,theta-(5*pi/6))) + + second_block_pose = sapien.Pose( + list(c2), euler2quat(0, 0, theta - (5 * np.pi / 6)) + ) second_block_size = [box1_half_w, box1_half_h, half_thickness] - # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) + # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) builder.add_box_visual( pose=second_block_pose, half_size=second_block_size, @@ -139,9 +154,11 @@ def create_goal_triangle(name="tri", base_color=None): ), ) - third_block_pose = sapien.Pose(list(c3),euler2quat(0,0,theta -(pi/6))) + third_block_pose = sapien.Pose( + list(c3), euler2quat(0, 0, theta - (np.pi / 6)) + ) third_block_size = [box1_half_w, box1_half_h, half_thickness] - # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) + # builder.add_box_collision(pose=second_block_pose, half_size=second_block_size) builder.add_box_visual( pose=third_block_pose, half_size=third_block_size, @@ -150,8 +167,7 @@ def create_goal_triangle(name="tri", base_color=None): ), ) return builder.build_kinematic(name=name) - - + # build a white canvas on the table self.canvas = self.scene.create_actor_builder() self.canvas.add_box_visual( @@ -207,18 +223,25 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.table_scene.initialize(env_idx) target_pos = torch.zeros((b, 3)) - target_pos[:,:2] = torch.rand((b,2))*0.02-0.1 - target_pos[:,-1] = 0.01 + target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1 + target_pos[:, -1] = 0.01 qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) - mats = quaternion_to_matrix(qs) - self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q= qs)) - - self.vertices = torch.from_numpy(np.tile(self.original_verts, (b, 1,1))) # b, 3, 3 - self.vertices = (mats.double() @ self.vertices.transpose(-1,-2).double()).transpose(-1,-2) # apply rotation matrix + mats = quaternion_to_matrix(qs) + self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + + self.vertices = torch.from_numpy( + np.tile(self.original_verts, (b, 1, 1)) + ) # b, 3, 3 + self.vertices = ( + mats.double() @ self.vertices.transpose(-1, -2).double() + ).transpose( + -1, -2 + ) # apply rotation matrix self.vertices += target_pos - - self.triangles = self.generate_triangle_with_points(100, self.vertices[:,:,:-1]) + self.triangles = self.generate_triangle_with_points( + 100, self.vertices[:, :, :-1] + ) for dot in self.dots: # initially spawn dots in the table so they aren't seen @@ -250,13 +273,13 @@ def _after_control_step(self): ) # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) - self.dots[self.draw_step].set_pose( - new_dot_pos - ) - if new_dot_pos.get_p()[:,-1] > 0: + self.dots[self.draw_step].set_pose(new_dot_pos) + if new_dot_pos.get_p()[:, -1] > 0: if self.dot_pos == None: self.dot_pos = new_dot_pos.get_p()[:, None, :] - self.dot_pos = torch.cat((self.dot_pos, new_dot_pos.get_p()[:, None,: ]), dim=1) + self.dot_pos = torch.cat( + (self.dot_pos, new_dot_pos.get_p()[:, None, :]), dim=1 + ) self.draw_step += 1 @@ -266,7 +289,7 @@ def _after_control_step(self): def evaluate(self): out = self.success_check() - return { "success" : out} + return {"success": out} def _get_obs_extra(self, info: Dict): return dict( @@ -275,31 +298,32 @@ def _get_obs_extra(self, info: Dict): def generate_triangle_with_points(self, n, vertices): batch_size = vertices.shape[0] - + all_points = [] for i in range(vertices.shape[1]): - start_vertex = vertices[:, i, :] - end_vertex = vertices[:, (i+1) % vertices.shape[1], :] - t = torch.linspace(0, 1, n+2, device=vertices.device)[:-1] - t = t.view(1, -1, 1).repeat(batch_size, 1, 2) - intermediate_points = start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t + start_vertex = vertices[:, i, :] + end_vertex = vertices[:, (i + 1) % vertices.shape[1], :] + t = torch.linspace(0, 1, n + 2, device=vertices.device)[:-1] + t = t.view(1, -1, 1).repeat(batch_size, 1, 2) + intermediate_points = ( + start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t + ) all_points.append(intermediate_points) - all_points = torch.cat(all_points, dim=1) + all_points = torch.cat(all_points, dim=1) return all_points def success_check(self): if self.dot_pos == None or len(self.dot_pos) == 0: return torch.Tensor([False]).to(bool) - drawn_pts = self.dot_pos[:,:,:-1] + drawn_pts = self.dot_pos[:, :, :-1] distance_matrix = torch.sqrt( torch.sum( - (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, - axis=-1 + (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 ) ) - Y_closeness = torch.min(distance_matrix, dim = 1).values < self.THRESHOLD + Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD return torch.Tensor([torch.all(Y_closeness)]).to(bool) diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py index 5066fbc38..a0373cbfc 100644 --- a/mani_skill/examples/motionplanning/panda_stick/run.py +++ b/mani_skill/examples/motionplanning/panda_stick/run.py @@ -1,30 +1,88 @@ import argparse +import os.path as osp + import gymnasium as gym import numpy as np -from tqdm import tqdm -import os.path as osp +from mani_skill.examples.motionplanning.panda_stick.solutions import \ + solveDrawTriangle from mani_skill.utils.wrappers.record import RecordEpisode -from mani_skill.examples.motionplanning.panda_stick.solutions import solveDrawTriangle +from tqdm import tqdm + +MP_SOLUTIONS = {"DrawTriangle-v1": solveDrawTriangle} + -MP_SOLUTIONS = { - "DrawTriangle-v1" : solveDrawTriangle -} def parse_args(args=None): parser = argparse.ArgumentParser() - parser.add_argument("-e", "--env-id", type=str, default="DrawTriangle-v1", help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}") - parser.add_argument("-o", "--obs-mode", type=str, default="none", help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.") - parser.add_argument("-n", "--num-traj", type=int, default=10, help="Number of trajectories to generate.") - parser.add_argument("--only-count-success", action="store_true", help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos") + parser.add_argument( + "-e", + "--env-id", + type=str, + default="DrawTriangle-v1", + help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}", + ) + parser.add_argument( + "-o", + "--obs-mode", + type=str, + default="none", + help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.", + ) + parser.add_argument( + "-n", + "--num-traj", + type=int, + default=10, + help="Number of trajectories to generate.", + ) + parser.add_argument( + "--only-count-success", + action="store_true", + help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos", + ) parser.add_argument("--reward-mode", type=str) - parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'") - parser.add_argument("--render-mode", type=str, default="rgb_array", help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos") - parser.add_argument("--vis", action="store_true", help="whether or not to open a GUI to visualize the solution live") - parser.add_argument("--save-video", action="store_true", help="whether or not to save videos locally") - parser.add_argument("--traj-name", type=str, help="The name of the trajectory .h5 file that will be created.") - parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer") - parser.add_argument("--record-dir", type=str, default="demos", help="where to save the recorded trajectories") + parser.add_argument( + "-b", + "--sim-backend", + type=str, + default="auto", + help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'", + ) + parser.add_argument( + "--render-mode", + type=str, + default="rgb_array", + help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos", + ) + parser.add_argument( + "--vis", + action="store_true", + help="whether or not to open a GUI to visualize the solution live", + ) + parser.add_argument( + "--save-video", + action="store_true", + help="whether or not to save videos locally", + ) + parser.add_argument( + "--traj-name", + type=str, + help="The name of the trajectory .h5 file that will be created.", + ) + parser.add_argument( + "--shader", + default="default", + type=str, + help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer", + ) + parser.add_argument( + "--record-dir", + type=str, + default="demos", + help="where to save the recorded trajectories", + ) return parser.parse_args() + def main(args): env_id = args.env_id env = gym.make( @@ -34,15 +92,18 @@ def main(args): render_mode="rgb_array", ) if env_id not in MP_SOLUTIONS: - raise RuntimeError(f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}") + raise RuntimeError( + f"No already written motion planning solutions for {env_id}. Available options are {list(MP_SOLUTIONS.keys())}" + ) env = RecordEpisode( env, output_dir=osp.join(args.record_dir, env_id, "motionplanning"), - trajectory_name=args.traj_name, save_video=args.save_video, + trajectory_name=args.traj_name, + save_video=args.save_video, source_type="motionplanning", source_desc="official motion planning solution from ManiSkill contributors", video_fps=30, - save_on_reset=False + save_on_reset=False, ) solve = MP_SOLUTIONS[env_id] print(f"Motion Planning Running on {env_id}") @@ -88,5 +149,6 @@ def main(args): break env.close() + if __name__ == "__main__": - main(parse_args()) \ No newline at end of file + main(parse_args()) diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py index 8186dd53f..8ad0262c7 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py @@ -1 +1 @@ -from .draw_triangle import solve as solveDrawTriangle \ No newline at end of file +from .draw_triangle import solve as solveDrawTriangle diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py index 29d7c5d9a..fbabb3f83 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py @@ -1,10 +1,10 @@ import numpy as np import sapien -from PIL import Image - from mani_skill.envs.tasks import PushCubeEnv from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ PandaStickMotionPlanningSolver +from PIL import Image + def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): env.reset(seed=seed) @@ -20,22 +20,21 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): FINGER_LENGTH = 0.025 env = env.unwrapped - rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) - reach_pose = sapien.Pose(p=list(env.vertices[0,0].numpy()), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot) res = planner.move_to_pose_with_screw(reach_pose) # -------------------------------------------------------------------------- # # Move to second vertex # -------------------------------------------------------------------------- # - reach_pose = sapien.Pose(p=list(env.vertices[0,1]), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) - - reach_pose = sapien.Pose(p=list(env.vertices[0,2]), q = rot) + + reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) - reach_pose = sapien.Pose(p=list(env.vertices[0,0]), q = rot) + reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) planner.close() - return res \ No newline at end of file + return res From 645a74c7818fd608683b75337a491b3a72fa1a42 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 15 Oct 2024 09:08:16 +0000 Subject: [PATCH 05/39] rgbd diffusion policy progress --- .../diffusion_policy/make_env.py | 3 +- .../diffusion_policy/plain_conv.py | 57 +++ .../diffusion_policy/utils.py | 97 ++++ .../baselines/diffusion_policy/train_rgbd.py | 451 ++++++++++++++++++ 4 files changed, 607 insertions(+), 1 deletion(-) create mode 100644 examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py create mode 100644 examples/baselines/diffusion_policy/train_rgbd.py diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index 13b6d0add..d26064b37 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -28,7 +28,8 @@ def thunk(): env = CPUGymWrapper(env, ignore_terminations=True, record_metrics=True) if video_dir: env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout") - env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon']) + if env_kwargs["obs_mode"] == "state": + env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon']) env.action_space.seed(seed) env.observation_space.seed(seed) return env diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py new file mode 100644 index 000000000..74e0da439 --- /dev/null +++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py @@ -0,0 +1,57 @@ +import torch.nn as nn + +def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True): + c_in = in_channels + module_list = [] + for idx, c_out in enumerate(mlp_channels): + module_list.append(nn.Linear(c_in, c_out)) + if last_act or idx < len(mlp_channels) - 1: + module_list.append(act_builder()) + c_in = c_out + return nn.Sequential(*module_list) + +class PlainConv(nn.Module): + def __init__(self, + in_channels=3, + out_dim=256, + pool_feature_map=False, + last_act=True, # True for ConvBody, False for CNN + ): + super().__init__() + # assume input image size is 64x64 + + self.out_dim = out_dim + self.cnn = nn.Sequential( + nn.Conv2d(in_channels, 16, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # [32, 32] + nn.Conv2d(16, 32, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # [16, 16] + nn.Conv2d(32, 64, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # [8, 8] + nn.Conv2d(64, 128, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), # [4, 4] + nn.Conv2d(128, 128, 1, padding=0, bias=True), nn.ReLU(inplace=True), + ) + + if pool_feature_map: + self.pool = nn.AdaptiveMaxPool2d((1, 1)) + self.fc = make_mlp(128, [out_dim], last_act=last_act) + else: + self.pool = None + self.fc = make_mlp(128 * 4 * 4, [out_dim], last_act=last_act) + + self.reset_parameters() + + def reset_parameters(self): + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d)): + if module.bias is not None: + nn.init.zeros_(module.bias) + + def forward(self, image): + x = self.cnn(image) + if self.pool is not None: + x = self.pool(x) + x = x.flatten(1) + x = self.fc(x) + return x \ No newline at end of file diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py index af3876fca..ee21344f4 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py @@ -1,7 +1,10 @@ from torch.utils.data.sampler import Sampler import numpy as np import torch +import torch.nn as nn from h5py import File, Group, Dataset +from gymnasium import spaces +import torch.nn.functional as F class IterationBasedBatchSampler(Sampler): """Wraps a BatchSampler. @@ -117,3 +120,97 @@ def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, con else: print('Load', target_key, len(dataset[target_key]), type(dataset[target_key][0])) return dataset +def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor): + img_dict = obs['sensor_data'] + new_img_dict = { + key: transpose_fn( + concat_fn([v[key] for v in img_dict.values()]) + ) # (C, H, W) or (B, C, H, W) + for key in ['rgb', 'depth'] + } + # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32 + # new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16) + + # Unified version + states_to_stack = state_obs_extractor(obs) + for j in range(len(states_to_stack)): + if states_to_stack[j].dtype == np.float64: + states_to_stack[j] = states_to_stack[j].astype(np.float32) + try: + state = np.hstack(states_to_stack) + except: # dirty fix for concat trajectory of states + state = np.column_stack(states_to_stack) + if state.dtype == np.float64: + for x in states_to_stack: + print(x.shape, x.dtype) + import pdb; pdb.set_trace() + + out_dict = { + 'state': state, + 'rgb': new_img_dict['rgb'], + 'depth': new_img_dict['depth'], + } + return out_dict + +def build_obs_space(env, depth_dtype, state_obs_extractor): + # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env + obs_space = env.observation_space + + # Unified version + state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)]) + + single_img_space = next(iter(env.observation_space['image'].values())) + h, w, _ = single_img_space['rgb'].shape + n_images = len(env.observation_space['image']) + + return spaces.Dict({ + 'state': spaces.Box(-float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32), + 'rgb': spaces.Box(0, 255, shape=(n_images*3, h,w), dtype=np.uint8), + 'depth': spaces.Box(-float("inf"), float("inf"), shape=(n_images,h,w), dtype=depth_dtype), + }) + +def build_state_obs_extractor(env_id): + env_name = env_id.split('-')[0] + if env_name in ['TurnFaucet', 'StackCube']: + return lambda obs: list(obs['extra'].values()) + elif env_name == 'PushChair' or env_name == "PickCube": + return lambda obs: list(obs['agent'].values()) + list(obs['extra'].values()) + else: + raise NotImplementedError(f'Please tune state obs by yourself') + + +class RandomShiftsAug(nn.Module): + """ + from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py + """ + def __init__(self, pad): + super().__init__() + self.pad = pad + + def forward(self, x): + n, c, h, w = x.size() + assert h == w + padding = tuple([self.pad] * 4) + x = F.pad(x, padding, 'replicate') + eps = 1.0 / (h + 2 * self.pad) + arange = torch.linspace(-1.0 + eps, + 1.0 - eps, + h + 2 * self.pad, + device=x.device, + dtype=x.dtype)[:h] + arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2) + base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2) + base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1) + + shift = torch.randint(0, + 2 * self.pad + 1, + size=(n, 1, 1, 2), + device=x.device, + dtype=x.dtype) + shift *= 2.0 / (h + 2 * self.pad) + + grid = base_grid + shift + return F.grid_sample(x, + grid, + padding_mode='zeros', + align_corners=False) \ No newline at end of file diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py new file mode 100644 index 000000000..e7c992aa4 --- /dev/null +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -0,0 +1,451 @@ +ALGO_NAME = 'BC_Diffusion_rgbd_UNet' + +import argparse +from functools import partial +import os +import random +from distutils.util import strtobool +import time +import gymnasium as gym +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.functional as F +from torch.utils.tensorboard import SummaryWriter +from diffusion_policy.evaluate import evaluate +from diffusion_policy.plain_conv import PlainConv +from mani_skill.utils import gym_utils +from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper +from mani_skill.utils.registration import REGISTERED_ENVS + +from collections import defaultdict, deque + +from torch.utils.data.dataset import Dataset +from torch.utils.data.sampler import RandomSampler, BatchSampler +from torch.utils.data.dataloader import DataLoader +from diffusion_policy.utils import IterationBasedBatchSampler, build_state_obs_extractor, convert_obs, worker_init_fn +from diffusion_policy.make_env import make_eval_envs +from diffusers.schedulers.scheduling_ddpm import DDPMScheduler +from diffusers.training_utils import EMAModel +from diffusers.optimization import get_scheduler +from diffusion_policy.conditional_unet1d import ConditionalUnet1D +from dataclasses import dataclass, field +from typing import Optional, List +import tyro + +from gymnasium.wrappers.frame_stack import FrameStack, LazyFrames +from gymnasium import spaces +from gymnasium.spaces import Box + + +@dataclass +class Args: + exp_name: Optional[str] = None + """the name of this experiment""" + seed: int = 1 + """seed of the experiment""" + torch_deterministic: bool = True + """if toggled, `torch.backends.cudnn.deterministic=False`""" + cuda: bool = True + """if toggled, cuda will be enabled by default""" + track: bool = False + """if toggled, this experiment will be tracked with Weights and Biases""" + wandb_project_name: str = "ManiSkill" + """the wandb's project name""" + wandb_entity: Optional[str] = None + """the entity (team) of wandb's project""" + capture_video: bool = True + """whether to capture videos of the agent performances (check out `videos` folder)""" + + env_id: str = "PegInsertionSide-v0" + """the id of the environment""" + demo_path: str = 'data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5' + """the path of demo dataset (pkl or h5)""" + num_demos: Optional[int] = None + """number of trajectories to load from the demo dataset""" + total_iters: int = 1_000_000 + """total timesteps of the experiment""" + batch_size: int = 1024 + """the batch size of sample from the replay memory""" + + # Diffusion Policy specific arguments + lr: float = 1e-4 + """the learning rate of the diffusion policy""" + obs_horizon: int = 2 # Seems not very important in ManiSkill, 1, 2, 4 work well + act_horizon: int = 8 # Seems not very important in ManiSkill, 4, 8, 15 work well + pred_horizon: int = 16 # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal + diffusion_step_embed_dim: int = 64 # not very important + unet_dims: List[int] = field(default_factory=lambda: [64, 128, 256]) # default setting is about ~4.5M params + n_groups: int = 8 # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila + + # Environment/experiment specific arguments + max_episode_steps: Optional[int] = None + """Change the environments' max_episode_steps to this value. Sometimes necessary if the demonstrations being imitated are too short. Typically the default + max episode steps of environments in ManiSkill are tuned lower so reinforcement learning agents can learn faster.""" + log_freq: int = 1000 + """the frequency of logging the training metrics""" + eval_freq: int = 5000 + """the frequency of evaluating the agent on the evaluation environments""" + save_freq: Optional[int] = None + """the frequency of saving the model checkpoints. By default this is None and will only save checkpoints based on the best evaluation metrics.""" + num_eval_episodes: int = 100 + """the number of episodes to evaluate the agent on""" + num_eval_envs: int = 10 + """the number of parallel environments to evaluate the agent on""" + sim_backend: str = "cpu" + """the simulation backend to use for evaluation environments. can be "cpu" or "gpu""" + num_dataload_workers: int = 0 + """the number of workers to use for loading the training data in the torch dataloader""" + control_mode: str = 'pd_joint_delta_pos' + """the control mode to use for the evaluation environments. Must match the control mode of the demonstration dataset.""" + + # additional tags/configs for logging purposes to wandb and shared comparisons with other algorithms + demo_type: Optional[str] = None + +def reorder_keys(d, ref_dict): + out = dict() + for k, v in ref_dict.items(): + if isinstance(v, dict) or isinstance(v, spaces.Dict): + out[k] = reorder_keys(d[k], ref_dict[k]) + else: + out[k] = d[k] + return out + + + + +class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory + def __init__(self, data_path, obs_process_fn, obs_space, num_traj): + if data_path[-4:] == '.pkl': + raise NotImplementedError() + else: + from diffusion_policy.utils import load_demo_dataset + trajectories = load_demo_dataset(data_path, num_traj=num_traj, concat=False) + # trajectories['observations'] is a list of dict, each dict is a traj, with keys in obs_space, values with length L+1 + # trajectories['actions'] is a list of np.ndarray (L, act_dim) + + print('Raw trajectory loaded, start to pre-process the observations...') + + # Pre-process the observations, make them align with the obs returned by the obs_wrapper + obs_traj_dict_list = [] + for obs_traj_dict in trajectories['observations']: + _obs_traj_dict = reorder_keys(obs_traj_dict, obs_space) # key order in demo is different from key order in env obs + _obs_traj_dict = obs_process_fn(_obs_traj_dict) + _obs_traj_dict['depth'] = torch.Tensor(_obs_traj_dict['depth'].astype(np.float32) / 1024).to(torch.float16) + _obs_traj_dict['rgb'] = torch.from_numpy(_obs_traj_dict['rgb']) # still uint8 + _obs_traj_dict['state'] = torch.from_numpy(_obs_traj_dict['state']) + obs_traj_dict_list.append(_obs_traj_dict) + trajectories['observations'] = obs_traj_dict_list + self.obs_keys = list(_obs_traj_dict.keys()) + # Pre-process the actions + for i in range(len(trajectories['actions'])): + trajectories['actions'][i] = torch.Tensor(trajectories['actions'][i]) + print('Obs/action pre-processing is done, start to pre-compute the slice indices...') + + # Pre-compute all possible (traj_idx, start, end) tuples, this is very specific to Diffusion Policy + if 'delta_pos' in args.control_mode or args.control_mode == 'base_pd_joint_vel_arm_pd_joint_vel': + self.pad_action_arm = torch.zeros((trajectories['actions'][0].shape[1]-1,)) + # to make the arm stay still, we pad the action with 0 in 'delta_pos' control mode + # gripper action needs to be copied from the last action + else: + raise NotImplementedError(f'Control Mode {args.control_mode} not supported') + self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = args.obs_horizon, args.pred_horizon + self.slices = [] + num_traj = len(trajectories['actions']) + total_transitions = 0 + for traj_idx in range(num_traj): + L = trajectories['actions'][traj_idx].shape[0] + assert trajectories['observations'][traj_idx]['state'].shape[0] == L + 1 + total_transitions += L + + # |o|o| observations: 2 + # | |a|a|a|a|a|a|a|a| actions executed: 8 + # |p|p|p|p|p|p|p|p|p|p|p|p|p|p|p|p| actions predicted: 16 + pad_before = obs_horizon - 1 + # Pad before the trajectory, so the first action of an episode is in "actions executed" + # obs_horizon - 1 is the number of "not used actions" + pad_after = pred_horizon - obs_horizon + # Pad after the trajectory, so all the observations are utilized in training + # Note that in the original code, pad_after = act_horizon - 1, but I think this is not the best choice + self.slices += [ + (traj_idx, start, start + pred_horizon) for start in range(-pad_before, L - pred_horizon + pad_after) + ] # slice indices follow convention [start, end) + + print(f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}") + + self.trajectories = trajectories + + def __getitem__(self, index): + traj_idx, start, end = self.slices[index] + L, act_dim = self.trajectories['actions'][traj_idx].shape + + obs_traj = self.trajectories['observations'][traj_idx] + obs_seq = {} + for k, v in obs_traj.items(): + obs_seq[k] = v[max(0, start):start+self.obs_horizon] # start+self.obs_horizon is at least 1 + if start < 0: # pad before the trajectory + pad_obs_seq = torch.stack([obs_seq[k][0]]*abs(start), dim=0) + obs_seq[k] = torch.cat((pad_obs_seq, obs_seq[k]), dim=0) + # don't need to pad obs after the trajectory, see the above char drawing + + act_seq = self.trajectories['actions'][traj_idx][max(0, start):end] + if start < 0: # pad before the trajectory + act_seq = torch.cat([act_seq[0].repeat(-start, 1), act_seq], dim=0) + if end > L: # pad after the trajectory + gripper_action = act_seq[-1, -1] # assume gripper is with pos controller + pad_action = torch.cat((self.pad_action_arm, gripper_action[None]), dim=0) + act_seq = torch.cat([act_seq, pad_action.repeat(end-L, 1)], dim=0) + # making the robot (arm and gripper) stay still + assert obs_seq['state'].shape[0] == self.obs_horizon and act_seq.shape[0] == self.pred_horizon + return { + 'observations': obs_seq, + 'actions': act_seq, + } + + def __len__(self): + return len(self.slices) +class DictFrameStack(FrameStack): + def __init__( + self, + env: gym.Env, + num_stack: int, + lz4_compress: bool = False, + ): + """Observation wrapper that stacks the observations in a rolling manner. + + Args: + env (Env): The environment to apply the wrapper + num_stack (int): The number of frames to stack + lz4_compress (bool): Use lz4 to compress the frames internally + """ + gym.utils.RecordConstructorArgs.__init__( + self, num_stack=num_stack, lz4_compress=lz4_compress + ) + gym.ObservationWrapper.__init__(self, env) + + self.num_stack = num_stack + self.lz4_compress = lz4_compress + + self.frames = deque(maxlen=num_stack) + + new_observation_space = gym.spaces.Dict() + for k, v in self.observation_space.items(): + low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=1) + high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=1) + new_observation_space[k] = Box( + low=low, high=high, dtype=v.dtype + ) + self.observation_space = new_observation_space + + def observation(self, observation): + """Converts the wrappers current frames to lazy frames. + + Args: + observation: Ignored + + Returns: + :class:`LazyFrames` object for the wrapper's frame buffer, :attr:`self.frames` + """ + assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack) + return { + k: LazyFrames([x[k] for x in self.frames], self.lz4_compress) + for k in self.observation_space.keys() + } + +class Agent(nn.Module): + def __init__(self, env, args): + super().__init__() + self.obs_horizon = args.obs_horizon + self.act_horizon = args.act_horizon + self.pred_horizon = args.pred_horizon + assert len(env.single_observation_space['state'].shape) == 2 # (obs_horizon, obs_dim) + assert len(env.single_action_space.shape) == 1 # (act_dim, ) + assert (env.single_action_space.high == 1).all() and (env.single_action_space.low == -1).all() + # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well + self.act_dim = env.single_action_space.shape[0] + obs_state_dim = env.single_observation_space['state'].shape[1] + _, C, H, W = envs.single_observation_space['rgb'].shape + + visual_feature_dim = 256 + self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim) + self.noise_pred_net = ConditionalUnet1D( + input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care) + global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim), + diffusion_step_embed_dim=args.diffusion_step_embed_dim, + down_dims=args.unet_dims, + n_groups=args.n_groups, + ) + self.num_diffusion_iters = 100 + self.noise_scheduler = DDPMScheduler( + num_train_timesteps=self.num_diffusion_iters, + beta_schedule='squaredcos_cap_v2', # has big impact on performance, try not to change + clip_sample=True, # clip output to [-1,1] to improve stability + prediction_type='epsilon' # predict noise (instead of denoised action) + ) + + if args.random_shift > 0: + from diffusion_policy.utils import RandomShiftsAug + self.aug = RandomShiftsAug(args.random_shift) + + def encode_obs(self, obs_seq, eval_mode): + rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W) + depth = obs_seq['depth'].float() # (B, obs_horizon, 1*k, H, W) + img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k + img_seq = img_seq.flatten(end_dim=1) # (B*obs_horizon, C, H, W) + if hasattr(self, 'aug') and not eval_mode: + img_seq = self.aug(img_seq) # (B*obs_horizon, C, H, W) + visual_feature = self.visual_encoder(img_seq) # (B*obs_horizon, D) + visual_feature = visual_feature.reshape(rgb.shape[0], self.obs_horizon, visual_feature.shape[1]) # (B, obs_horizon, D) + feature = torch.cat((visual_feature, obs_seq['state']), dim=-1) # (B, obs_horizon, D+obs_state_dim) + return feature.flatten(start_dim=1) # (B, obs_horizon * (D+obs_state_dim)) + + def compute_loss(self, obs_seq, action_seq): + B = obs_seq['state'].shape[0] + + # observation as FiLM conditioning + obs_cond = self.encode_obs(obs_seq, eval_mode=False) # (B, obs_horizon * obs_dim) + + # sample noise to add to actions + noise = torch.randn((B, self.pred_horizon, self.act_dim), device=device) + + # sample a diffusion iteration for each data point + timesteps = torch.randint( + 0, self.noise_scheduler.config.num_train_timesteps, + (B,), device=device + ).long() + + # add noise to the clean images(actions) according to the noise magnitude at each diffusion iteration + # (this is the forward diffusion process) + noisy_action_seq = self.noise_scheduler.add_noise( + action_seq, noise, timesteps) + + # predict the noise residual + noise_pred = self.noise_pred_net( + noisy_action_seq, timesteps, global_cond=obs_cond) + + return F.mse_loss(noise_pred, noise) + + def get_eval_action(self, obs_seq): + # init scheduler + # self.noise_scheduler.set_timesteps(self.num_diffusion_iters) + # set_timesteps will change noise_scheduler.timesteps is only used in noise_scheduler.step() + # noise_scheduler.step() is only called during inference + # if we use DDPM, and inference_diffusion_steps == train_diffusion_steps, then we can skip this + + # obs_seq['state']: (B, obs_horizon, obs_state_dim) + B = obs_seq['state'].shape[0] + with torch.no_grad(): + obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim) + + # initialize action from Guassian noise + noisy_action_seq = torch.randn((B, self.pred_horizon, self.act_dim), device=obs_seq['state'].device) + + for k in self.noise_scheduler.timesteps: + # predict noise + noise_pred = self.noise_pred_net( + sample=noisy_action_seq, + timestep=k, + global_cond=obs_cond, + ) + + # inverse diffusion step (remove noise) + noisy_action_seq = self.noise_scheduler.step( + model_output=noise_pred, + timestep=k, + sample=noisy_action_seq, + ).prev_sample + + # only take act_horizon number of actions + start = self.obs_horizon - 1 + end = start + self.act_horizon + return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim) + +if __name__ == "__main__": + args = tyro.cli(Args) + + if args.exp_name is None: + args.exp_name = os.path.basename(__file__)[: -len(".py")] + run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" + else: + run_name = args.exp_name + + if args.demo_path.endswith('.h5'): + import json + json_file = args.demo_path[:-2] + 'json' + with open(json_file, 'r') as f: + demo_info = json.load(f) + if 'control_mode' in demo_info['env_info']['env_kwargs']: + control_mode = demo_info['env_info']['env_kwargs']['control_mode'] + elif 'control_mode' in demo_info['episodes'][0]: + control_mode = demo_info['episodes'][0]['control_mode'] + else: + raise Exception('Control mode not found in json') + assert control_mode == args.control_mode, f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}" + assert args.obs_horizon + args.act_horizon - 1 <= args.pred_horizon + assert args.obs_horizon >= 1 and args.act_horizon >= 1 and args.pred_horizon >= 1 + + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + torch.backends.cudnn.deterministic = args.torch_deterministic + + device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") + + env_kwargs = dict(control_mode=args.control_mode, reward_mode="sparse", obs_mode="rgbd", render_mode="rgb_array") + if args.max_episode_steps is not None: + env_kwargs["max_episode_steps"] = args.max_episode_steps + other_kwargs = dict(obs_horizon=args.obs_horizon) + envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[FlattenRGBDObservationWrapper, partial(DictFrameStack, num_stack = args.obs_horizon)]) + if args.track: + import wandb + config = vars(args) + config["eval_env_cfg"] = dict(**env_kwargs, num_envs=args.num_eval_envs, env_id=args.env_id, env_horizon=gym_utils.find_max_episode_steps_value(envs)) + wandb.init( + project=args.wandb_project_name, + entity=args.wandb_entity, + sync_tensorboard=True, + config=config, + name=run_name, + save_code=True, + group="DiffusionPolicy", + tags=["diffusion_policy"] + ) + writer = SummaryWriter(f"runs/{run_name}") + writer.add_text( + "hyperparameters", + "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), + ) + + obs_process_fn = partial( + convert_obs, + concat_fn=partial(np.concatenate, axis=-1), + transpose_fn=partial(np.transpose, axes=(0, 3, 1, 2)), # (B, H, W, C) -> (B, C, H, W) + state_obs_extractor=build_state_obs_extractor(args.env_id), + ) + tmp_env = gym.make(args.env_id, obs_mode='rgbd'); orignal_obs_space = tmp_env.observation_space; tmp_env.close() + dataset = SmallDemoDataset_DiffusionPolicy(args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos) + sampler = RandomSampler(dataset, replacement=False) + batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True) + batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters) + train_dataloader = DataLoader( + dataset, + batch_sampler=batch_sampler, + num_workers=args.num_dataload_workers, + worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed), + pin_memory=True, + persistent_workers=(args.num_dataload_workers > 0), + ) + sampler = RandomSampler(dataset, replacement=False) + batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True) + batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters) + train_dataloader = DataLoader( + dataset, + batch_sampler=batch_sampler, + num_workers=args.num_dataload_workers, + worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed), + pin_memory=True, + persistent_workers=(args.num_dataload_workers > 0), + ) + + agent = Agent(envs, args).to(device) \ No newline at end of file From 2bd9cfcf4a944161ee342b37f7661213443f5b2d Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 17 Oct 2024 01:53:49 +0000 Subject: [PATCH 06/39] diff policy rgbd cpu fixes --- .../diffusion_policy/make_env.py | 56 +++++++ .../diffusion_policy/plain_conv.py | 2 +- .../baselines/diffusion_policy/train_rgbd.py | 152 +++++++++++------- mani_skill/utils/wrappers/flatten.py | 10 +- 4 files changed, 158 insertions(+), 62 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index d26064b37..e9e5bf33a 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -1,9 +1,63 @@ +from collections import deque from typing import Optional import gymnasium as gym import mani_skill.envs from mani_skill.utils import gym_utils from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv from mani_skill.utils.wrappers import RecordEpisode, FrameStack, CPUGymWrapper +import numpy as np +from gymnasium.spaces import Box +from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack +from gymnasium.wrappers.frame_stack import LazyFrames + + +class DictFrameStack(GymFrameStack): + def __init__( + self, + env: gym.Env, + num_stack: int, + lz4_compress: bool = False, + ): + """Observation wrapper that stacks the observations in a rolling manner. + + Args: + env (Env): The environment to apply the wrapper + num_stack (int): The number of frames to stack + lz4_compress (bool): Use lz4 to compress the frames internally + """ + gym.utils.RecordConstructorArgs.__init__( + self, num_stack=num_stack, lz4_compress=lz4_compress + ) + gym.ObservationWrapper.__init__(self, env) + + self.num_stack = num_stack + self.lz4_compress = lz4_compress + + self.frames = deque(maxlen=num_stack) + + new_observation_space = gym.spaces.Dict() + for k, v in self.observation_space.items(): + low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=0) + high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=0) + new_observation_space[k] = Box( + low=low, high=high, dtype=v.dtype + ) + self.observation_space = new_observation_space + + def observation(self, observation): + """Converts the wrappers current frames to lazy frames. + + Args: + observation: Ignored + + Returns: + :class:`LazyFrames` object for the wrapper's frame buffer, :attr:`self.frames` + """ + assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack) + return { + k: LazyFrames([x[k] for x in self.frames], self.lz4_compress) + for k in self.observation_space.keys() + } def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, other_kwargs: dict, video_dir: Optional[str] = None, wrappers: list[gym.Wrapper] = []): @@ -30,6 +84,8 @@ def thunk(): env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout") if env_kwargs["obs_mode"] == "state": env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon']) + elif env_kwargs["obs_mode"] == "rgbd": + env = DictFrameStack(env, 2) env.action_space.seed(seed) env.observation_space.seed(seed) return env diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py index 74e0da439..50c09e59d 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py @@ -38,7 +38,7 @@ def __init__(self, self.fc = make_mlp(128, [out_dim], last_act=last_act) else: self.pool = None - self.fc = make_mlp(128 * 4 * 4, [out_dim], last_act=last_act) + self.fc = make_mlp(128 * 4 * 4 * 4, [out_dim], last_act=last_act) self.reset_parameters() diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index e7c992aa4..2d7f9719b 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -1,6 +1,7 @@ ALGO_NAME = 'BC_Diffusion_rgbd_UNet' import argparse +from collections import defaultdict from functools import partial import os import random @@ -19,7 +20,6 @@ from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper from mani_skill.utils.registration import REGISTERED_ENVS -from collections import defaultdict, deque from torch.utils.data.dataset import Dataset from torch.utils.data.sampler import RandomSampler, BatchSampler @@ -33,10 +33,7 @@ from dataclasses import dataclass, field from typing import Optional, List import tyro - -from gymnasium.wrappers.frame_stack import FrameStack, LazyFrames from gymnasium import spaces -from gymnasium.spaces import Box @dataclass @@ -66,7 +63,7 @@ class Args: """number of trajectories to load from the demo dataset""" total_iters: int = 1_000_000 """total timesteps of the experiment""" - batch_size: int = 1024 + batch_size: int = 256 """the batch size of sample from the replay memory""" # Diffusion Policy specific arguments @@ -205,53 +202,7 @@ def __getitem__(self, index): def __len__(self): return len(self.slices) -class DictFrameStack(FrameStack): - def __init__( - self, - env: gym.Env, - num_stack: int, - lz4_compress: bool = False, - ): - """Observation wrapper that stacks the observations in a rolling manner. - - Args: - env (Env): The environment to apply the wrapper - num_stack (int): The number of frames to stack - lz4_compress (bool): Use lz4 to compress the frames internally - """ - gym.utils.RecordConstructorArgs.__init__( - self, num_stack=num_stack, lz4_compress=lz4_compress - ) - gym.ObservationWrapper.__init__(self, env) - - self.num_stack = num_stack - self.lz4_compress = lz4_compress - - self.frames = deque(maxlen=num_stack) - - new_observation_space = gym.spaces.Dict() - for k, v in self.observation_space.items(): - low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=1) - high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=1) - new_observation_space[k] = Box( - low=low, high=high, dtype=v.dtype - ) - self.observation_space = new_observation_space - def observation(self, observation): - """Converts the wrappers current frames to lazy frames. - - Args: - observation: Ignored - - Returns: - :class:`LazyFrames` object for the wrapper's frame buffer, :attr:`self.frames` - """ - assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack) - return { - k: LazyFrames([x[k] for x in self.frames], self.lz4_compress) - for k in self.observation_space.keys() - } class Agent(nn.Module): def __init__(self, env, args): @@ -265,10 +216,10 @@ def __init__(self, env, args): # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well self.act_dim = env.single_action_space.shape[0] obs_state_dim = env.single_observation_space['state'].shape[1] - _, C, H, W = envs.single_observation_space['rgb'].shape + _, H, W, C = envs.single_observation_space['rgb'].shape visual_feature_dim = 256 - self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim) + self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim, pool_feature_map=True) self.noise_pred_net = ConditionalUnet1D( input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care) global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim), @@ -284,9 +235,6 @@ def __init__(self, env, args): prediction_type='epsilon' # predict noise (instead of denoised action) ) - if args.random_shift > 0: - from diffusion_policy.utils import RandomShiftsAug - self.aug = RandomShiftsAug(args.random_shift) def encode_obs(self, obs_seq, eval_mode): rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W) @@ -326,7 +274,7 @@ def compute_loss(self, obs_seq, action_seq): return F.mse_loss(noise_pred, noise) - def get_eval_action(self, obs_seq): + def get_action(self, obs_seq): # init scheduler # self.noise_scheduler.set_timesteps(self.num_diffusion_iters) # set_timesteps will change noise_scheduler.timesteps is only used in noise_scheduler.step() @@ -336,6 +284,9 @@ def get_eval_action(self, obs_seq): # obs_seq['state']: (B, obs_horizon, obs_state_dim) B = obs_seq['state'].shape[0] with torch.no_grad(): + obs_seq['rgb'] = obs_seq['rgb'].permute(0,1,4,2,3) + obs_seq['depth'] = obs_seq['depth'].permute(0,1,4,2,3) + obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim) # initialize action from Guassian noise @@ -396,7 +347,7 @@ def get_eval_action(self, obs_seq): if args.max_episode_steps is not None: env_kwargs["max_episode_steps"] = args.max_episode_steps other_kwargs = dict(obs_horizon=args.obs_horizon) - envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[FlattenRGBDObservationWrapper, partial(DictFrameStack, num_stack = args.obs_horizon)]) + envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)]) if args.track: import wandb config = vars(args) @@ -448,4 +399,87 @@ def get_eval_action(self, obs_seq): persistent_workers=(args.num_dataload_workers > 0), ) - agent = Agent(envs, args).to(device) \ No newline at end of file + agent = Agent(envs, args).to(device) + + optimizer = optim.AdamW(params=agent.parameters(), + lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6) + + # Cosine LR schedule with linear warmup + lr_scheduler = get_scheduler( + name='cosine', + optimizer=optimizer, + num_warmup_steps=500, + num_training_steps=args.total_iters, + ) + + # Exponential Moving Average + # accelerates training and improves stability + # holds a copy of the model weights + ema = EMAModel(parameters=agent.parameters(), power=0.75) + ema_agent = Agent(envs, args).to(device) + + # ---------------------------------------------------------------------------- # + # Training begins. + # ---------------------------------------------------------------------------- # + agent.train() + + best_eval_metrics = defaultdict(float) + timings = defaultdict(float) + + for iteration, data_batch in enumerate(train_dataloader): + # # copy data from cpu to gpu + # data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()} + + # forward and compute loss + obs_batch_dict = data_batch['observations'] + obs_batch_dict = {k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items()} + act_batch = data_batch['actions'].cuda(non_blocking=True) + + # forward and compute loss + total_loss = agent.compute_loss( + obs_seq=obs_batch_dict, # obs_batch_dict['state'] is (B, L, obs_dim) + action_seq=act_batch, # (B, L, act_dim) + ) + + # backward + optimizer.zero_grad() + total_loss.backward() + optimizer.step() + lr_scheduler.step() # step lr scheduler every batch, this is different from standard pytorch behavior + last_tick = time.time() + + ema.step(agent.parameters()) + + if iteration % args.log_freq == 0: + print(f"Iteration {iteration}, loss: {total_loss.item()}") + writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], iteration) + writer.add_scalar("losses/total_loss", total_loss.item(), iteration) + for k, v in timings.items(): + writer.add_scalar(f"time/{k}", v, iteration) + # Evaluation + if iteration % args.eval_freq == 0: + last_tick = time.time() + + ema.copy_to(ema_agent.parameters()) + # def sample_fn(obs): + + eval_metrics = evaluate(args.num_eval_episodes, ema_agent, envs, device, args.sim_backend) + timings["eval"] += time.time() - last_tick + + print(f"Evaluated {len(eval_metrics['success_at_end'])} episodes") + for k in eval_metrics.keys(): + eval_metrics[k] = np.mean(eval_metrics[k]) + writer.add_scalar(f"eval/{k}", eval_metrics[k], iteration) + print(f"{k}: {eval_metrics[k]:.4f}") + + save_on_best_metrics = ["success_once", "success_at_end"] + for k in save_on_best_metrics: + if k in eval_metrics and eval_metrics[k] > best_eval_metrics[k]: + best_eval_metrics[k] = eval_metrics[k] + save_ckpt(run_name, f"best_eval_{k}") + print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.') + # Checkpoint + if args.save_freq is not None and iteration % args.save_freq == 0: + save_ckpt(run_name, str(iteration)) + envs.close() + writer.close() \ No newline at end of file diff --git a/mani_skill/utils/wrappers/flatten.py b/mani_skill/utils/wrappers/flatten.py index 64be9d0c4..126dcbe82 100644 --- a/mani_skill/utils/wrappers/flatten.py +++ b/mani_skill/utils/wrappers/flatten.py @@ -23,11 +23,12 @@ class FlattenRGBDObservationWrapper(gym.ObservationWrapper): Note that the returned observations will have a "rgbd" or "rgb" or "depth" key depending on the rgb/depth bool flags. """ - def __init__(self, env, rgb=True, depth=True, state=True) -> None: + def __init__(self, env, rgb=True, depth=True, state=True, sep_depth=False) -> None: self.base_env: BaseEnv = env.unwrapped super().__init__(env) self.include_rgb = rgb self.include_depth = depth + self.sep_depth = sep_depth self.include_state = state new_obs = self.observation(self.base_env._init_raw_obs) self.base_env.update_obs_space(new_obs) @@ -50,7 +51,12 @@ def observation(self, observation: Dict): if self.include_rgb and not self.include_depth: ret["rgb"] = images elif self.include_rgb and self.include_depth: - ret["rgbd"] = images + # todo: might not be the best way or the right place to perform this + if self.sep_depth: + ret["rgb"] = images[...,:-1] + ret["depth"] = images[...,-1:] + else: + ret["rgbd"] = images elif self.include_depth and not self.include_rgb: ret["depth"] = images return ret From 41bc78a323f56ed4c660c28bfb98430d06b53ad6 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 21 Oct 2024 01:25:01 +0000 Subject: [PATCH 07/39] minor diff policy fixes and finished draw triangle parallelization --- .../diffusion_policy/diffusion_policy/make_env.py | 2 +- examples/baselines/diffusion_policy/train_rgbd.py | 1 + mani_skill/envs/tasks/drawing/draw_triangle.py | 9 ++++----- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index e9e5bf33a..7cfb657f5 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -85,7 +85,7 @@ def thunk(): if env_kwargs["obs_mode"] == "state": env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon']) elif env_kwargs["obs_mode"] == "rgbd": - env = DictFrameStack(env, 2) + env = DictFrameStack(env, other_kwargs['obs_horizon']) env.action_space.seed(seed) env.observation_space.seed(seed) return env diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index 2d7f9719b..f2706c609 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -478,6 +478,7 @@ def get_action(self, obs_seq): best_eval_metrics[k] = eval_metrics[k] save_ckpt(run_name, f"best_eval_{k}") print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.') + # Checkpoint if args.save_freq is not None and iteration % args.save_freq == 0: save_ckpt(run_name, str(iteration)) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 5c8c39351..74e6a63bd 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -43,7 +43,7 @@ class DrawTriangle(BaseEnv): """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" THRESHOLD = 0.05 - SUPPORTED_REWARD_MODES = ["none"] + SUPPORTED_REWARD_MODES = ["sparse"] SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore agent: PandaStick @@ -216,7 +216,6 @@ def create_goal_triangle(name="tri", base_color=None): ) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): - # NOTE (stao): for simplicity this task cannot handle partial resets self.draw_step = 0 with torch.device(self.device): b = len(env_idx) @@ -226,18 +225,18 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1 target_pos[:, -1] = 0.01 qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) - mats = quaternion_to_matrix(qs) + mats = quaternion_to_matrix(qs).to(self.device) self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) self.vertices = torch.from_numpy( np.tile(self.original_verts, (b, 1, 1)) - ) # b, 3, 3 + ).to(self.device) # b, 3, 3 self.vertices = ( mats.double() @ self.vertices.transpose(-1, -2).double() ).transpose( -1, -2 ) # apply rotation matrix - self.vertices += target_pos + self.vertices += target_pos.unsqueeze(1) self.triangles = self.generate_triangle_with_points( 100, self.vertices[:, :, :-1] From e322dadd33fc6d1e4455fe6be778ef40cf1c85ff Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 21 Oct 2024 19:55:42 +0000 Subject: [PATCH 08/39] Added depth arg to diff pol rgbd + formatting --- .../diffusion_policy/make_env.py | 79 +++- .../diffusion_policy/plain_conv.py | 32 +- .../diffusion_policy/utils.py | 221 ++++++----- .../baselines/diffusion_policy/train_rgbd.py | 372 +++++++++++------- .../envs/tasks/drawing/draw_triangle.py | 4 +- .../panda_stick/motionplanner.py | 4 +- 6 files changed, 443 insertions(+), 269 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index 7cfb657f5..0a210d79f 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -1,14 +1,15 @@ from collections import deque from typing import Optional + import gymnasium as gym import mani_skill.envs -from mani_skill.utils import gym_utils -from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv -from mani_skill.utils.wrappers import RecordEpisode, FrameStack, CPUGymWrapper import numpy as np from gymnasium.spaces import Box from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack from gymnasium.wrappers.frame_stack import LazyFrames +from mani_skill.utils import gym_utils +from mani_skill.utils.wrappers import CPUGymWrapper, FrameStack, RecordEpisode +from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv class DictFrameStack(GymFrameStack): @@ -39,9 +40,7 @@ def __init__( for k, v in self.observation_space.items(): low = np.repeat(v.low[np.newaxis, ...], num_stack, axis=0) high = np.repeat(v.high[np.newaxis, ...], num_stack, axis=0) - new_observation_space[k] = Box( - low=low, high=high, dtype=v.dtype - ) + new_observation_space[k] = Box(low=low, high=high, dtype=v.dtype) self.observation_space = new_observation_space def observation(self, observation): @@ -60,7 +59,15 @@ def observation(self, observation): } -def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, other_kwargs: dict, video_dir: Optional[str] = None, wrappers: list[gym.Wrapper] = []): +def make_eval_envs( + env_id, + num_envs: int, + sim_backend: str, + env_kwargs: dict, + other_kwargs: dict, + video_dir: Optional[str] = None, + wrappers: list[gym.Wrapper] = [], +): """Create vectorized environment for evaluation and/or recording videos. For CPU vectorized environments only the first parallel environment is used to record videos. For GPU vectorized environments all parallel environments are used to record videos. @@ -74,32 +81,72 @@ def make_eval_envs(env_id, num_envs: int, sim_backend: str, env_kwargs: dict, ot wrappers: the list of wrappers to apply to the environment. """ if sim_backend == "cpu": - def cpu_make_env(env_id, seed, video_dir=None, env_kwargs = dict(), other_kwargs = dict()): + + def cpu_make_env( + env_id, seed, video_dir=None, env_kwargs=dict(), other_kwargs=dict() + ): def thunk(): env = gym.make(env_id, reconfiguration_freq=1, **env_kwargs) for wrapper in wrappers: env = wrapper(env) env = CPUGymWrapper(env, ignore_terminations=True, record_metrics=True) if video_dir: - env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, info_on_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout") + env = RecordEpisode( + env, + output_dir=video_dir, + save_trajectory=False, + info_on_video=True, + source_type="diffusion_policy", + source_desc="diffusion_policy evaluation rollout", + ) if env_kwargs["obs_mode"] == "state": - env = gym.wrappers.FrameStack(env, other_kwargs['obs_horizon']) + env = gym.wrappers.FrameStack(env, other_kwargs["obs_horizon"]) elif env_kwargs["obs_mode"] == "rgbd": - env = DictFrameStack(env, other_kwargs['obs_horizon']) + env = DictFrameStack(env, other_kwargs["obs_horizon"]) env.action_space.seed(seed) env.observation_space.seed(seed) return env return thunk - vector_cls = gym.vector.SyncVectorEnv if num_envs == 1 else lambda x : gym.vector.AsyncVectorEnv(x, context="forkserver") - env = vector_cls([cpu_make_env(env_id, seed, video_dir if seed == 0 else None, env_kwargs, other_kwargs) for seed in range(num_envs)]) + + vector_cls = ( + gym.vector.SyncVectorEnv + if num_envs == 1 + else lambda x: gym.vector.AsyncVectorEnv(x, context="forkserver") + ) + env = vector_cls( + [ + cpu_make_env( + env_id, + seed, + video_dir if seed == 0 else None, + env_kwargs, + other_kwargs, + ) + for seed in range(num_envs) + ] + ) else: - env = gym.make(env_id, num_envs=num_envs, sim_backend=sim_backend, reconfiguration_freq=1, **env_kwargs) + env = gym.make( + env_id, + num_envs=num_envs, + sim_backend=sim_backend, + reconfiguration_freq=1, + **env_kwargs + ) max_episode_steps = gym_utils.find_max_episode_steps_value(env) for wrapper in wrappers: env = wrapper(env) - env = FrameStack(env, num_stack=other_kwargs['obs_horizon']) + env = FrameStack(env, num_stack=other_kwargs["obs_horizon"]) if video_dir: - env = RecordEpisode(env, output_dir=video_dir, save_trajectory=False, save_video=True, source_type="diffusion_policy", source_desc="diffusion_policy evaluation rollout", max_steps_per_video=max_episode_steps) + env = RecordEpisode( + env, + output_dir=video_dir, + save_trajectory=False, + save_video=True, + source_type="diffusion_policy", + source_desc="diffusion_policy evaluation rollout", + max_steps_per_video=max_episode_steps, + ) env = ManiSkillVectorEnv(env, ignore_terminations=True, record_metrics=True) return env diff --git a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py index 50c09e59d..7244469b8 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/plain_conv.py @@ -1,5 +1,6 @@ import torch.nn as nn + def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True): c_in = in_channels module_list = [] @@ -10,27 +11,34 @@ def make_mlp(in_channels, mlp_channels, act_builder=nn.ReLU, last_act=True): c_in = c_out return nn.Sequential(*module_list) + class PlainConv(nn.Module): - def __init__(self, - in_channels=3, - out_dim=256, - pool_feature_map=False, - last_act=True, # True for ConvBody, False for CNN - ): + def __init__( + self, + in_channels=3, + out_dim=256, + pool_feature_map=False, + last_act=True, # True for ConvBody, False for CNN + ): super().__init__() # assume input image size is 64x64 self.out_dim = out_dim self.cnn = nn.Sequential( - nn.Conv2d(in_channels, 16, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.Conv2d(in_channels, 16, 3, padding=1, bias=True), + nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [32, 32] - nn.Conv2d(16, 32, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.Conv2d(16, 32, 3, padding=1, bias=True), + nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [16, 16] - nn.Conv2d(32, 64, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.Conv2d(32, 64, 3, padding=1, bias=True), + nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [8, 8] - nn.Conv2d(64, 128, 3, padding=1, bias=True), nn.ReLU(inplace=True), + nn.Conv2d(64, 128, 3, padding=1, bias=True), + nn.ReLU(inplace=True), nn.MaxPool2d(2, 2), # [4, 4] - nn.Conv2d(128, 128, 1, padding=0, bias=True), nn.ReLU(inplace=True), + nn.Conv2d(128, 128, 1, padding=0, bias=True), + nn.ReLU(inplace=True), ) if pool_feature_map: @@ -54,4 +62,4 @@ def forward(self, image): x = self.pool(x) x = x.flatten(1) x = self.fc(x) - return x \ No newline at end of file + return x diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py index ee21344f4..bc270e19c 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py @@ -1,10 +1,11 @@ -from torch.utils.data.sampler import Sampler import numpy as np import torch import torch.nn as nn -from h5py import File, Group, Dataset -from gymnasium import spaces import torch.nn.functional as F +from gymnasium import spaces +from h5py import Dataset, File, Group +from torch.utils.data.sampler import Sampler + class IterationBasedBatchSampler(Sampler): """Wraps a BatchSampler. @@ -48,15 +49,18 @@ def worker_init_fn(worker_id, base_seed=None): # print(worker_id, base_seed) np.random.seed(base_seed + worker_id) + TARGET_KEY_TO_SOURCE_KEY = { - 'states': 'env_states', - 'observations': 'obs', - 'success': 'success', - 'next_observations': 'obs', + "states": "env_states", + "observations": "obs", + "success": "success", + "next_observations": "obs", # 'dones': 'dones', # 'rewards': 'rewards', - 'actions': 'actions', + "actions": "actions", } + + def load_content_from_h5_file(file): if isinstance(file, (File, Group)): return {key: load_content_from_h5_file(file[key]) for key in list(file.keys())} @@ -65,34 +69,40 @@ def load_content_from_h5_file(file): else: raise NotImplementedError(f"Unspported h5 file type: {type(file)}") -def load_hdf5(path, ): - print('Loading HDF5 file', path) - file = File(path, 'r') + +def load_hdf5( + path, +): + print("Loading HDF5 file", path) + file = File(path, "r") ret = load_content_from_h5_file(file) file.close() - print('Loaded') + print("Loaded") return ret + def load_traj_hdf5(path, num_traj=None): - print('Loading HDF5 file', path) - file = File(path, 'r') + print("Loading HDF5 file", path) + file = File(path, "r") keys = list(file.keys()) if num_traj is not None: assert num_traj <= len(keys), f"num_traj: {num_traj} > len(keys): {len(keys)}" - keys = sorted(keys, key=lambda x: int(x.split('_')[-1])) + keys = sorted(keys, key=lambda x: int(x.split("_")[-1])) keys = keys[:num_traj] - ret = { - key: load_content_from_h5_file(file[key]) for key in keys - } + ret = {key: load_content_from_h5_file(file[key]) for key in keys} file.close() - print('Loaded') + print("Loaded") return ret -def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, concat=True): + + +def load_demo_dataset( + path, keys=["observations", "actions"], num_traj=None, concat=True +): # assert num_traj is None raw_data = load_traj_hdf5(path, num_traj) # raw_data has keys like: ['traj_0', 'traj_1', ...] # raw_data['traj_0'] has keys like: ['actions', 'dones', 'env_states', 'infos', ...] - _traj = raw_data['traj_0'] + _traj = raw_data["traj_0"] for key in keys: source_key = TARGET_KEY_TO_SOURCE_KEY[key] assert source_key in _traj, f"key: {source_key} not in traj_0: {_traj.keys()}" @@ -101,88 +111,108 @@ def load_demo_dataset(path, keys=['observations', 'actions'], num_traj=None, con # if 'next' in target_key: # raise NotImplementedError('Please carefully deal with the length of trajectory') source_key = TARGET_KEY_TO_SOURCE_KEY[target_key] - dataset[target_key] = [ raw_data[idx][source_key] for idx in raw_data ] + dataset[target_key] = [raw_data[idx][source_key] for idx in raw_data] if isinstance(dataset[target_key][0], np.ndarray) and concat: - if target_key in ['observations', 'states'] and \ - len(dataset[target_key][0]) > len(raw_data['traj_0']['actions']): - dataset[target_key] = np.concatenate([ - t[:-1] for t in dataset[target_key] - ], axis=0) - elif target_key in ['next_observations', 'next_states'] and \ - len(dataset[target_key][0]) > len(raw_data['traj_0']['actions']): - dataset[target_key] = np.concatenate([ - t[1:] for t in dataset[target_key] - ], axis=0) + if target_key in ["observations", "states"] and len( + dataset[target_key][0] + ) > len(raw_data["traj_0"]["actions"]): + dataset[target_key] = np.concatenate( + [t[:-1] for t in dataset[target_key]], axis=0 + ) + elif target_key in ["next_observations", "next_states"] and len( + dataset[target_key][0] + ) > len(raw_data["traj_0"]["actions"]): + dataset[target_key] = np.concatenate( + [t[1:] for t in dataset[target_key]], axis=0 + ) else: dataset[target_key] = np.concatenate(dataset[target_key], axis=0) - print('Load', target_key, dataset[target_key].shape) + print("Load", target_key, dataset[target_key].shape) else: - print('Load', target_key, len(dataset[target_key]), type(dataset[target_key][0])) + print( + "Load", + target_key, + len(dataset[target_key]), + type(dataset[target_key][0]), + ) return dataset -def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor): - img_dict = obs['sensor_data'] - new_img_dict = { - key: transpose_fn( - concat_fn([v[key] for v in img_dict.values()]) - ) # (C, H, W) or (B, C, H, W) - for key in ['rgb', 'depth'] - } - # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32 - # new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16) - - # Unified version - states_to_stack = state_obs_extractor(obs) - for j in range(len(states_to_stack)): - if states_to_stack[j].dtype == np.float64: - states_to_stack[j] = states_to_stack[j].astype(np.float32) - try: - state = np.hstack(states_to_stack) - except: # dirty fix for concat trajectory of states - state = np.column_stack(states_to_stack) - if state.dtype == np.float64: - for x in states_to_stack: - print(x.shape, x.dtype) - import pdb; pdb.set_trace() - - out_dict = { - 'state': state, - 'rgb': new_img_dict['rgb'], - 'depth': new_img_dict['depth'], - } - return out_dict -def build_obs_space(env, depth_dtype, state_obs_extractor): - # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env - obs_space = env.observation_space - # Unified version - state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)]) +def convert_obs(obs, concat_fn, transpose_fn, state_obs_extractor): + img_dict = obs["sensor_data"] + new_img_dict = { + key: transpose_fn( + concat_fn([v[key] for v in img_dict.values()]) + ) # (C, H, W) or (B, C, H, W) + for key in ["rgb", "depth"] + } + # if isinstance(new_img_dict['depth'], torch.Tensor): # MS2 vec env uses float16, but gym AsyncVecEnv uses float32 + # new_img_dict['depth'] = new_img_dict['depth'].to(torch.float16) + + # Unified version + states_to_stack = state_obs_extractor(obs) + for j in range(len(states_to_stack)): + if states_to_stack[j].dtype == np.float64: + states_to_stack[j] = states_to_stack[j].astype(np.float32) + try: + state = np.hstack(states_to_stack) + except: # dirty fix for concat trajectory of states + state = np.column_stack(states_to_stack) + if state.dtype == np.float64: + for x in states_to_stack: + print(x.shape, x.dtype) + import pdb + + pdb.set_trace() + + out_dict = { + "state": state, + "rgb": new_img_dict["rgb"], + "depth": new_img_dict["depth"], + } + return out_dict + - single_img_space = next(iter(env.observation_space['image'].values())) - h, w, _ = single_img_space['rgb'].shape - n_images = len(env.observation_space['image']) +def build_obs_space(env, depth_dtype, state_obs_extractor): + # NOTE: We have to use float32 for gym AsyncVecEnv since it does not support float16, but we can use float16 for MS2 vec env + obs_space = env.observation_space + + # Unified version + state_dim = sum([v.shape[0] for v in state_obs_extractor(obs_space)]) + + single_img_space = next(iter(env.observation_space["image"].values())) + h, w, _ = single_img_space["rgb"].shape + n_images = len(env.observation_space["image"]) + + return spaces.Dict( + { + "state": spaces.Box( + -float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32 + ), + "rgb": spaces.Box(0, 255, shape=(n_images * 3, h, w), dtype=np.uint8), + "depth": spaces.Box( + -float("inf"), float("inf"), shape=(n_images, h, w), dtype=depth_dtype + ), + } + ) - return spaces.Dict({ - 'state': spaces.Box(-float("inf"), float("inf"), shape=(state_dim,), dtype=np.float32), - 'rgb': spaces.Box(0, 255, shape=(n_images*3, h,w), dtype=np.uint8), - 'depth': spaces.Box(-float("inf"), float("inf"), shape=(n_images,h,w), dtype=depth_dtype), - }) def build_state_obs_extractor(env_id): - env_name = env_id.split('-')[0] - if env_name in ['TurnFaucet', 'StackCube']: - return lambda obs: list(obs['extra'].values()) - elif env_name == 'PushChair' or env_name == "PickCube": - return lambda obs: list(obs['agent'].values()) + list(obs['extra'].values()) + env_name = env_id.split("-")[0] + if env_name in ["TurnFaucet", "StackCube"]: + return lambda obs: list(obs["extra"].values()) + elif env_name == "PushChair" or env_name == "PickCube": + return lambda obs: list(obs["agent"].values()) + list(obs["extra"].values()) else: - raise NotImplementedError(f'Please tune state obs by yourself') + raise NotImplementedError(f"Please tune state obs by yourself") class RandomShiftsAug(nn.Module): """ from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py """ + def __init__(self, pad): super().__init__() self.pad = pad @@ -191,26 +221,19 @@ def forward(self, x): n, c, h, w = x.size() assert h == w padding = tuple([self.pad] * 4) - x = F.pad(x, padding, 'replicate') + x = F.pad(x, padding, "replicate") eps = 1.0 / (h + 2 * self.pad) - arange = torch.linspace(-1.0 + eps, - 1.0 - eps, - h + 2 * self.pad, - device=x.device, - dtype=x.dtype)[:h] + arange = torch.linspace( + -1.0 + eps, 1.0 - eps, h + 2 * self.pad, device=x.device, dtype=x.dtype + )[:h] arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2) base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2) base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1) - shift = torch.randint(0, - 2 * self.pad + 1, - size=(n, 1, 1, 2), - device=x.device, - dtype=x.dtype) + shift = torch.randint( + 0, 2 * self.pad + 1, size=(n, 1, 1, 2), device=x.device, dtype=x.dtype + ) shift *= 2.0 / (h + 2 * self.pad) grid = base_grid + shift - return F.grid_sample(x, - grid, - padding_mode='zeros', - align_corners=False) \ No newline at end of file + return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False) diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index f2706c609..ef6e317f6 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -1,39 +1,41 @@ -ALGO_NAME = 'BC_Diffusion_rgbd_UNet' +ALGO_NAME = "BC_Diffusion_rgbd_UNet" import argparse -from collections import defaultdict -from functools import partial import os import random -from distutils.util import strtobool import time +from collections import defaultdict +from dataclasses import dataclass, field +from distutils.util import strtobool +from functools import partial +from typing import List, Optional + import gymnasium as gym import numpy as np import torch import torch.nn as nn -import torch.optim as optim import torch.nn.functional as F -from torch.utils.tensorboard import SummaryWriter -from diffusion_policy.evaluate import evaluate -from diffusion_policy.plain_conv import PlainConv +import torch.optim as optim +import tyro +from diffusers.optimization import get_scheduler +from diffusers.schedulers.scheduling_ddpm import DDPMScheduler +from diffusers.training_utils import EMAModel +from gymnasium import spaces from mani_skill.utils import gym_utils -from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper from mani_skill.utils.registration import REGISTERED_ENVS - - -from torch.utils.data.dataset import Dataset -from torch.utils.data.sampler import RandomSampler, BatchSampler +from mani_skill.utils.wrappers.flatten import FlattenRGBDObservationWrapper from torch.utils.data.dataloader import DataLoader -from diffusion_policy.utils import IterationBasedBatchSampler, build_state_obs_extractor, convert_obs, worker_init_fn -from diffusion_policy.make_env import make_eval_envs -from diffusers.schedulers.scheduling_ddpm import DDPMScheduler -from diffusers.training_utils import EMAModel -from diffusers.optimization import get_scheduler +from torch.utils.data.dataset import Dataset +from torch.utils.data.sampler import BatchSampler, RandomSampler +from torch.utils.tensorboard import SummaryWriter + from diffusion_policy.conditional_unet1d import ConditionalUnet1D -from dataclasses import dataclass, field -from typing import Optional, List -import tyro -from gymnasium import spaces +from diffusion_policy.evaluate import evaluate +from diffusion_policy.make_env import make_eval_envs +from diffusion_policy.plain_conv import PlainConv +from diffusion_policy.utils import (IterationBasedBatchSampler, + build_state_obs_extractor, convert_obs, + worker_init_fn) @dataclass @@ -57,7 +59,9 @@ class Args: env_id: str = "PegInsertionSide-v0" """the id of the environment""" - demo_path: str = 'data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5' + demo_path: str = ( + "data/ms2_official_demos/rigid_body/PegInsertionSide-v0/trajectory.state.pd_ee_delta_pose.h5" + ) """the path of demo dataset (pkl or h5)""" num_demos: Optional[int] = None """number of trajectories to load from the demo dataset""" @@ -69,12 +73,20 @@ class Args: # Diffusion Policy specific arguments lr: float = 1e-4 """the learning rate of the diffusion policy""" - obs_horizon: int = 2 # Seems not very important in ManiSkill, 1, 2, 4 work well - act_horizon: int = 8 # Seems not very important in ManiSkill, 4, 8, 15 work well - pred_horizon: int = 16 # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal - diffusion_step_embed_dim: int = 64 # not very important - unet_dims: List[int] = field(default_factory=lambda: [64, 128, 256]) # default setting is about ~4.5M params - n_groups: int = 8 # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila + obs_horizon: int = 2 # Seems not very important in ManiSkill, 1, 2, 4 work well + act_horizon: int = 8 # Seems not very important in ManiSkill, 4, 8, 15 work well + pred_horizon: int = ( + 16 # 16->8 leads to worse performance, maybe it is like generate a half image; 16->32, improvement is very marginal + ) + diffusion_step_embed_dim: int = 64 # not very important + unet_dims: List[int] = field( + default_factory=lambda: [64, 128, 256] + ) # default setting is about ~4.5M params + n_groups: int = ( + 8 # jigu says it is better to let each group have at least 8 channels; it seems 4 and 8 are simila + ) + depth: bool = True + """use depth to train""" # Environment/experiment specific arguments max_episode_steps: Optional[int] = None @@ -94,12 +106,13 @@ class Args: """the simulation backend to use for evaluation environments. can be "cpu" or "gpu""" num_dataload_workers: int = 0 """the number of workers to use for loading the training data in the torch dataloader""" - control_mode: str = 'pd_joint_delta_pos' + control_mode: str = "pd_joint_delta_pos" """the control mode to use for the evaluation environments. Must match the control mode of the demonstration dataset.""" # additional tags/configs for logging purposes to wandb and shared comparisons with other algorithms demo_type: Optional[str] = None + def reorder_keys(d, ref_dict): out = dict() for k, v in ref_dict.items(): @@ -110,50 +123,65 @@ def reorder_keys(d, ref_dict): return out - - -class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory +class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory def __init__(self, data_path, obs_process_fn, obs_space, num_traj): - if data_path[-4:] == '.pkl': + if data_path[-4:] == ".pkl": raise NotImplementedError() else: from diffusion_policy.utils import load_demo_dataset + trajectories = load_demo_dataset(data_path, num_traj=num_traj, concat=False) # trajectories['observations'] is a list of dict, each dict is a traj, with keys in obs_space, values with length L+1 # trajectories['actions'] is a list of np.ndarray (L, act_dim) - print('Raw trajectory loaded, start to pre-process the observations...') + print("Raw trajectory loaded, start to pre-process the observations...") # Pre-process the observations, make them align with the obs returned by the obs_wrapper obs_traj_dict_list = [] - for obs_traj_dict in trajectories['observations']: - _obs_traj_dict = reorder_keys(obs_traj_dict, obs_space) # key order in demo is different from key order in env obs + for obs_traj_dict in trajectories["observations"]: + _obs_traj_dict = reorder_keys( + obs_traj_dict, obs_space + ) # key order in demo is different from key order in env obs _obs_traj_dict = obs_process_fn(_obs_traj_dict) - _obs_traj_dict['depth'] = torch.Tensor(_obs_traj_dict['depth'].astype(np.float32) / 1024).to(torch.float16) - _obs_traj_dict['rgb'] = torch.from_numpy(_obs_traj_dict['rgb']) # still uint8 - _obs_traj_dict['state'] = torch.from_numpy(_obs_traj_dict['state']) + _obs_traj_dict["depth"] = torch.Tensor( + _obs_traj_dict["depth"].astype(np.float32) / 1024 + ).to(torch.float16) + _obs_traj_dict["rgb"] = torch.from_numpy( + _obs_traj_dict["rgb"] + ) # still uint8 + _obs_traj_dict["state"] = torch.from_numpy(_obs_traj_dict["state"]) obs_traj_dict_list.append(_obs_traj_dict) - trajectories['observations'] = obs_traj_dict_list + trajectories["observations"] = obs_traj_dict_list self.obs_keys = list(_obs_traj_dict.keys()) # Pre-process the actions - for i in range(len(trajectories['actions'])): - trajectories['actions'][i] = torch.Tensor(trajectories['actions'][i]) - print('Obs/action pre-processing is done, start to pre-compute the slice indices...') + for i in range(len(trajectories["actions"])): + trajectories["actions"][i] = torch.Tensor(trajectories["actions"][i]) + print( + "Obs/action pre-processing is done, start to pre-compute the slice indices..." + ) # Pre-compute all possible (traj_idx, start, end) tuples, this is very specific to Diffusion Policy - if 'delta_pos' in args.control_mode or args.control_mode == 'base_pd_joint_vel_arm_pd_joint_vel': - self.pad_action_arm = torch.zeros((trajectories['actions'][0].shape[1]-1,)) + if ( + "delta_pos" in args.control_mode + or args.control_mode == "base_pd_joint_vel_arm_pd_joint_vel" + ): + self.pad_action_arm = torch.zeros( + (trajectories["actions"][0].shape[1] - 1,) + ) # to make the arm stay still, we pad the action with 0 in 'delta_pos' control mode # gripper action needs to be copied from the last action else: - raise NotImplementedError(f'Control Mode {args.control_mode} not supported') - self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = args.obs_horizon, args.pred_horizon + raise NotImplementedError(f"Control Mode {args.control_mode} not supported") + self.obs_horizon, self.pred_horizon = obs_horizon, pred_horizon = ( + args.obs_horizon, + args.pred_horizon, + ) self.slices = [] - num_traj = len(trajectories['actions']) + num_traj = len(trajectories["actions"]) total_transitions = 0 for traj_idx in range(num_traj): - L = trajectories['actions'][traj_idx].shape[0] - assert trajectories['observations'][traj_idx]['state'].shape[0] == L + 1 + L = trajectories["actions"][traj_idx].shape[0] + assert trajectories["observations"][traj_idx]["state"].shape[0] == L + 1 total_transitions += L # |o|o| observations: 2 @@ -166,38 +194,46 @@ def __init__(self, data_path, obs_process_fn, obs_space, num_traj): # Pad after the trajectory, so all the observations are utilized in training # Note that in the original code, pad_after = act_horizon - 1, but I think this is not the best choice self.slices += [ - (traj_idx, start, start + pred_horizon) for start in range(-pad_before, L - pred_horizon + pad_after) + (traj_idx, start, start + pred_horizon) + for start in range(-pad_before, L - pred_horizon + pad_after) ] # slice indices follow convention [start, end) - - print(f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}") + + print( + f"Total transitions: {total_transitions}, Total obs sequences: {len(self.slices)}" + ) self.trajectories = trajectories def __getitem__(self, index): traj_idx, start, end = self.slices[index] - L, act_dim = self.trajectories['actions'][traj_idx].shape + L, act_dim = self.trajectories["actions"][traj_idx].shape - obs_traj = self.trajectories['observations'][traj_idx] + obs_traj = self.trajectories["observations"][traj_idx] obs_seq = {} for k, v in obs_traj.items(): - obs_seq[k] = v[max(0, start):start+self.obs_horizon] # start+self.obs_horizon is at least 1 - if start < 0: # pad before the trajectory - pad_obs_seq = torch.stack([obs_seq[k][0]]*abs(start), dim=0) + obs_seq[k] = v[ + max(0, start) : start + self.obs_horizon + ] # start+self.obs_horizon is at least 1 + if start < 0: # pad before the trajectory + pad_obs_seq = torch.stack([obs_seq[k][0]] * abs(start), dim=0) obs_seq[k] = torch.cat((pad_obs_seq, obs_seq[k]), dim=0) # don't need to pad obs after the trajectory, see the above char drawing - act_seq = self.trajectories['actions'][traj_idx][max(0, start):end] - if start < 0: # pad before the trajectory + act_seq = self.trajectories["actions"][traj_idx][max(0, start) : end] + if start < 0: # pad before the trajectory act_seq = torch.cat([act_seq[0].repeat(-start, 1), act_seq], dim=0) - if end > L: # pad after the trajectory - gripper_action = act_seq[-1, -1] # assume gripper is with pos controller + if end > L: # pad after the trajectory + gripper_action = act_seq[-1, -1] # assume gripper is with pos controller pad_action = torch.cat((self.pad_action_arm, gripper_action[None]), dim=0) - act_seq = torch.cat([act_seq, pad_action.repeat(end-L, 1)], dim=0) + act_seq = torch.cat([act_seq, pad_action.repeat(end - L, 1)], dim=0) # making the robot (arm and gripper) stay still - assert obs_seq['state'].shape[0] == self.obs_horizon and act_seq.shape[0] == self.pred_horizon + assert ( + obs_seq["state"].shape[0] == self.obs_horizon + and act_seq.shape[0] == self.pred_horizon + ) return { - 'observations': obs_seq, - 'actions': act_seq, + "observations": obs_seq, + "actions": act_seq, } def __len__(self): @@ -210,18 +246,25 @@ def __init__(self, env, args): self.obs_horizon = args.obs_horizon self.act_horizon = args.act_horizon self.pred_horizon = args.pred_horizon - assert len(env.single_observation_space['state'].shape) == 2 # (obs_horizon, obs_dim) - assert len(env.single_action_space.shape) == 1 # (act_dim, ) - assert (env.single_action_space.high == 1).all() and (env.single_action_space.low == -1).all() + assert ( + len(env.single_observation_space["state"].shape) == 2 + ) # (obs_horizon, obs_dim) + assert len(env.single_action_space.shape) == 1 # (act_dim, ) + assert (env.single_action_space.high == 1).all() and ( + env.single_action_space.low == -1 + ).all() # denoising results will be clipped to [-1,1], so the action should be in [-1,1] as well self.act_dim = env.single_action_space.shape[0] - obs_state_dim = env.single_observation_space['state'].shape[1] - _, H, W, C = envs.single_observation_space['rgb'].shape + obs_state_dim = env.single_observation_space["state"].shape[1] + _, H, W, C = envs.single_observation_space["rgb"].shape visual_feature_dim = 256 - self.visual_encoder = PlainConv(in_channels=int(C/3*4), out_dim=visual_feature_dim, pool_feature_map=True) + in_c = int(C / 3 * 4) if args.depth else C + self.visual_encoder = PlainConv( + in_channels=in_c, out_dim=visual_feature_dim, pool_feature_map=True + ) self.noise_pred_net = ConditionalUnet1D( - input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care) + input_dim=self.act_dim, # act_horizon is not used (U-Net doesn't care) global_cond_dim=self.obs_horizon * (visual_feature_dim + obs_state_dim), diffusion_step_embed_dim=args.diffusion_step_embed_dim, down_dims=args.unet_dims, @@ -230,50 +273,57 @@ def __init__(self, env, args): self.num_diffusion_iters = 100 self.noise_scheduler = DDPMScheduler( num_train_timesteps=self.num_diffusion_iters, - beta_schedule='squaredcos_cap_v2', # has big impact on performance, try not to change - clip_sample=True, # clip output to [-1,1] to improve stability - prediction_type='epsilon' # predict noise (instead of denoised action) + beta_schedule="squaredcos_cap_v2", # has big impact on performance, try not to change + clip_sample=True, # clip output to [-1,1] to improve stability + prediction_type="epsilon", # predict noise (instead of denoised action) ) - def encode_obs(self, obs_seq, eval_mode): - rgb = obs_seq['rgb'].float() / 255.0 # (B, obs_horizon, 3*k, H, W) - depth = obs_seq['depth'].float() # (B, obs_horizon, 1*k, H, W) - img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k - img_seq = img_seq.flatten(end_dim=1) # (B*obs_horizon, C, H, W) - if hasattr(self, 'aug') and not eval_mode: - img_seq = self.aug(img_seq) # (B*obs_horizon, C, H, W) - visual_feature = self.visual_encoder(img_seq) # (B*obs_horizon, D) - visual_feature = visual_feature.reshape(rgb.shape[0], self.obs_horizon, visual_feature.shape[1]) # (B, obs_horizon, D) - feature = torch.cat((visual_feature, obs_seq['state']), dim=-1) # (B, obs_horizon, D+obs_state_dim) - return feature.flatten(start_dim=1) # (B, obs_horizon * (D+obs_state_dim)) - + rgb = obs_seq["rgb"].float() / 255.0 # (B, obs_horizon, 3*k, H, W) + if args.depth: + depth = obs_seq["depth"].float() # (B, obs_horizon, 1*k, H, W) + img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k + else: + img_seq = rgb + img_seq = img_seq.flatten(end_dim=1) # (B*obs_horizon, C, H, W) + if hasattr(self, "aug") and not eval_mode: + img_seq = self.aug(img_seq) # (B*obs_horizon, C, H, W) + visual_feature = self.visual_encoder(img_seq) # (B*obs_horizon, D) + visual_feature = visual_feature.reshape( + rgb.shape[0], self.obs_horizon, visual_feature.shape[1] + ) # (B, obs_horizon, D) + feature = torch.cat( + (visual_feature, obs_seq["state"]), dim=-1 + ) # (B, obs_horizon, D+obs_state_dim) + return feature.flatten(start_dim=1) # (B, obs_horizon * (D+obs_state_dim)) + def compute_loss(self, obs_seq, action_seq): - B = obs_seq['state'].shape[0] + B = obs_seq["state"].shape[0] # observation as FiLM conditioning - obs_cond = self.encode_obs(obs_seq, eval_mode=False) # (B, obs_horizon * obs_dim) + obs_cond = self.encode_obs( + obs_seq, eval_mode=False + ) # (B, obs_horizon * obs_dim) # sample noise to add to actions noise = torch.randn((B, self.pred_horizon, self.act_dim), device=device) # sample a diffusion iteration for each data point timesteps = torch.randint( - 0, self.noise_scheduler.config.num_train_timesteps, - (B,), device=device + 0, self.noise_scheduler.config.num_train_timesteps, (B,), device=device ).long() # add noise to the clean images(actions) according to the noise magnitude at each diffusion iteration # (this is the forward diffusion process) - noisy_action_seq = self.noise_scheduler.add_noise( - action_seq, noise, timesteps) - + noisy_action_seq = self.noise_scheduler.add_noise(action_seq, noise, timesteps) + # predict the noise residual noise_pred = self.noise_pred_net( - noisy_action_seq, timesteps, global_cond=obs_cond) + noisy_action_seq, timesteps, global_cond=obs_cond + ) return F.mse_loss(noise_pred, noise) - + def get_action(self, obs_seq): # init scheduler # self.noise_scheduler.set_timesteps(self.num_diffusion_iters) @@ -282,15 +332,20 @@ def get_action(self, obs_seq): # if we use DDPM, and inference_diffusion_steps == train_diffusion_steps, then we can skip this # obs_seq['state']: (B, obs_horizon, obs_state_dim) - B = obs_seq['state'].shape[0] + B = obs_seq["state"].shape[0] with torch.no_grad(): - obs_seq['rgb'] = obs_seq['rgb'].permute(0,1,4,2,3) - obs_seq['depth'] = obs_seq['depth'].permute(0,1,4,2,3) + obs_seq["rgb"] = obs_seq["rgb"].permute(0, 1, 4, 2, 3) + if args.depth: + obs_seq["depth"] = obs_seq["depth"].permute(0, 1, 4, 2, 3) / 1024 - obs_cond = self.encode_obs(obs_seq, eval_mode=True) # (B, obs_horizon * obs_dim) + obs_cond = self.encode_obs( + obs_seq, eval_mode=True + ) # (B, obs_horizon * obs_dim) # initialize action from Guassian noise - noisy_action_seq = torch.randn((B, self.pred_horizon, self.act_dim), device=obs_seq['state'].device) + noisy_action_seq = torch.randn( + (B, self.pred_horizon, self.act_dim), device=obs_seq["state"].device + ) for k in self.noise_scheduler.timesteps: # predict noise @@ -310,7 +365,8 @@ def get_action(self, obs_seq): # only take act_horizon number of actions start = self.obs_horizon - 1 end = start + self.act_horizon - return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim) + return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim) + if __name__ == "__main__": args = tyro.cli(Args) @@ -320,19 +376,22 @@ def get_action(self, obs_seq): run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}" else: run_name = args.exp_name - - if args.demo_path.endswith('.h5'): + + if args.demo_path.endswith(".h5"): import json - json_file = args.demo_path[:-2] + 'json' - with open(json_file, 'r') as f: + + json_file = args.demo_path[:-2] + "json" + with open(json_file, "r") as f: demo_info = json.load(f) - if 'control_mode' in demo_info['env_info']['env_kwargs']: - control_mode = demo_info['env_info']['env_kwargs']['control_mode'] - elif 'control_mode' in demo_info['episodes'][0]: - control_mode = demo_info['episodes'][0]['control_mode'] + if "control_mode" in demo_info["env_info"]["env_kwargs"]: + control_mode = demo_info["env_info"]["env_kwargs"]["control_mode"] + elif "control_mode" in demo_info["episodes"][0]: + control_mode = demo_info["episodes"][0]["control_mode"] else: - raise Exception('Control mode not found in json') - assert control_mode == args.control_mode, f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}" + raise Exception("Control mode not found in json") + assert ( + control_mode == args.control_mode + ), f"Control mode mismatched. Dataset has control mode {control_mode}, but args has control mode {args.control_mode}" assert args.obs_horizon + args.act_horizon - 1 <= args.pred_horizon assert args.obs_horizon >= 1 and args.act_horizon >= 1 and args.pred_horizon >= 1 @@ -343,15 +402,34 @@ def get_action(self, obs_seq): device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu") - env_kwargs = dict(control_mode=args.control_mode, reward_mode="sparse", obs_mode="rgbd", render_mode="rgb_array") + env_kwargs = dict( + control_mode=args.control_mode, + reward_mode="sparse", + obs_mode="rgbd", + render_mode="rgb_array", + ) if args.max_episode_steps is not None: env_kwargs["max_episode_steps"] = args.max_episode_steps other_kwargs = dict(obs_horizon=args.obs_horizon) - envs = make_eval_envs(args.env_id, args.num_eval_envs, args.sim_backend, env_kwargs, other_kwargs, video_dir=f'runs/{run_name}/videos' if args.capture_video else None, wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)]) + envs = make_eval_envs( + args.env_id, + args.num_eval_envs, + args.sim_backend, + env_kwargs, + other_kwargs, + video_dir=f"runs/{run_name}/videos" if args.capture_video else None, + wrappers=[partial(FlattenRGBDObservationWrapper, sep_depth=True)], + ) if args.track: import wandb + config = vars(args) - config["eval_env_cfg"] = dict(**env_kwargs, num_envs=args.num_eval_envs, env_id=args.env_id, env_horizon=gym_utils.find_max_episode_steps_value(envs)) + config["eval_env_cfg"] = dict( + **env_kwargs, + num_envs=args.num_eval_envs, + env_id=args.env_id, + env_horizon=gym_utils.find_max_episode_steps_value(envs), + ) wandb.init( project=args.wandb_project_name, entity=args.wandb_entity, @@ -360,22 +438,29 @@ def get_action(self, obs_seq): name=run_name, save_code=True, group="DiffusionPolicy", - tags=["diffusion_policy"] + tags=["diffusion_policy"], ) writer = SummaryWriter(f"runs/{run_name}") writer.add_text( "hyperparameters", - "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), + "|param|value|\n|-|-|\n%s" + % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])), ) obs_process_fn = partial( - convert_obs, - concat_fn=partial(np.concatenate, axis=-1), - transpose_fn=partial(np.transpose, axes=(0, 3, 1, 2)), # (B, H, W, C) -> (B, C, H, W) + convert_obs, + concat_fn=partial(np.concatenate, axis=-1), + transpose_fn=partial( + np.transpose, axes=(0, 3, 1, 2) + ), # (B, H, W, C) -> (B, C, H, W) state_obs_extractor=build_state_obs_extractor(args.env_id), ) - tmp_env = gym.make(args.env_id, obs_mode='rgbd'); orignal_obs_space = tmp_env.observation_space; tmp_env.close() - dataset = SmallDemoDataset_DiffusionPolicy(args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos) + tmp_env = gym.make(args.env_id, obs_mode="rgbd") + orignal_obs_space = tmp_env.observation_space + tmp_env.close() + dataset = SmallDemoDataset_DiffusionPolicy( + args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos + ) sampler = RandomSampler(dataset, replacement=False) batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True) batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters) @@ -401,12 +486,13 @@ def get_action(self, obs_seq): agent = Agent(envs, args).to(device) - optimizer = optim.AdamW(params=agent.parameters(), - lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6) + optimizer = optim.AdamW( + params=agent.parameters(), lr=args.lr, betas=(0.95, 0.999), weight_decay=1e-6 + ) # Cosine LR schedule with linear warmup lr_scheduler = get_scheduler( - name='cosine', + name="cosine", optimizer=optimizer, num_warmup_steps=500, num_training_steps=args.total_iters, @@ -422,7 +508,7 @@ def get_action(self, obs_seq): # Training begins. # ---------------------------------------------------------------------------- # agent.train() - + best_eval_metrics = defaultdict(float) timings = defaultdict(float) @@ -431,28 +517,32 @@ def get_action(self, obs_seq): # data_batch = {k: v.cuda(non_blocking=True) for k, v in data_batch.items()} # forward and compute loss - obs_batch_dict = data_batch['observations'] - obs_batch_dict = {k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items()} - act_batch = data_batch['actions'].cuda(non_blocking=True) + obs_batch_dict = data_batch["observations"] + obs_batch_dict = { + k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items() + } + act_batch = data_batch["actions"].cuda(non_blocking=True) # forward and compute loss total_loss = agent.compute_loss( - obs_seq=obs_batch_dict, # obs_batch_dict['state'] is (B, L, obs_dim) - action_seq=act_batch, # (B, L, act_dim) + obs_seq=obs_batch_dict, # obs_batch_dict['state'] is (B, L, obs_dim) + action_seq=act_batch, # (B, L, act_dim) ) # backward optimizer.zero_grad() total_loss.backward() optimizer.step() - lr_scheduler.step() # step lr scheduler every batch, this is different from standard pytorch behavior + lr_scheduler.step() # step lr scheduler every batch, this is different from standard pytorch behavior last_tick = time.time() ema.step(agent.parameters()) if iteration % args.log_freq == 0: print(f"Iteration {iteration}, loss: {total_loss.item()}") - writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], iteration) + writer.add_scalar( + "charts/learning_rate", optimizer.param_groups[0]["lr"], iteration + ) writer.add_scalar("losses/total_loss", total_loss.item(), iteration) for k, v in timings.items(): writer.add_scalar(f"time/{k}", v, iteration) @@ -463,7 +553,9 @@ def get_action(self, obs_seq): ema.copy_to(ema_agent.parameters()) # def sample_fn(obs): - eval_metrics = evaluate(args.num_eval_episodes, ema_agent, envs, device, args.sim_backend) + eval_metrics = evaluate( + args.num_eval_episodes, ema_agent, envs, device, args.sim_backend + ) timings["eval"] += time.time() - last_tick print(f"Evaluated {len(eval_metrics['success_at_end'])} episodes") @@ -477,10 +569,12 @@ def get_action(self, obs_seq): if k in eval_metrics and eval_metrics[k] > best_eval_metrics[k]: best_eval_metrics[k] = eval_metrics[k] save_ckpt(run_name, f"best_eval_{k}") - print(f'New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint.') - + print( + f"New best {k}_rate: {eval_metrics[k]:.4f}. Saving checkpoint." + ) + # Checkpoint if args.save_freq is not None and iteration % args.save_freq == 0: save_ckpt(run_name, str(iteration)) envs.close() - writer.close() \ No newline at end of file + writer.close() diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 74e6a63bd..38b3fc365 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -230,7 +230,9 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.vertices = torch.from_numpy( np.tile(self.original_verts, (b, 1, 1)) - ).to(self.device) # b, 3, 3 + ).to( + self.device + ) # b, 3, 3 self.vertices = ( mats.double() @ self.vertices.transpose(-1, -2).double() ).transpose( diff --git a/mani_skill/examples/motionplanning/panda_stick/motionplanner.py b/mani_skill/examples/motionplanning/panda_stick/motionplanner.py index 097ad1748..5a423884b 100644 --- a/mani_skill/examples/motionplanning/panda_stick/motionplanner.py +++ b/mani_skill/examples/motionplanning/panda_stick/motionplanner.py @@ -2,12 +2,12 @@ import numpy as np import sapien import trimesh - from mani_skill.agents.base_agent import BaseAgent from mani_skill.envs.sapien_env import BaseEnv from mani_skill.envs.scene import ManiSkillScene from mani_skill.utils.structs.pose import to_sapien_pose + class PandaStickMotionPlanningSolver: def __init__( self, @@ -113,7 +113,7 @@ def move_to_pose_with_screw( ): pose = to_sapien_pose(pose) # try screw two times before giving up - pose = sapien.Pose(p=pose.p , q=pose.q) + pose = sapien.Pose(p=pose.p, q=pose.q) result = self.planner.plan_screw( np.concatenate([pose.p, pose.q]), self.robot.get_qpos().cpu().numpy()[0], From 46c3b227650327df68b8b4034986efa576bb0148 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 21 Oct 2024 19:58:27 +0000 Subject: [PATCH 09/39] Removed unused code --- .../diffusion_policy/utils.py | 30 ------------------- 1 file changed, 30 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py index bc270e19c..e3398ccee 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py @@ -207,33 +207,3 @@ def build_state_obs_extractor(env_id): else: raise NotImplementedError(f"Please tune state obs by yourself") - -class RandomShiftsAug(nn.Module): - """ - from https://github.com/facebookresearch/drqv2/blob/main/drqv2.py - """ - - def __init__(self, pad): - super().__init__() - self.pad = pad - - def forward(self, x): - n, c, h, w = x.size() - assert h == w - padding = tuple([self.pad] * 4) - x = F.pad(x, padding, "replicate") - eps = 1.0 / (h + 2 * self.pad) - arange = torch.linspace( - -1.0 + eps, 1.0 - eps, h + 2 * self.pad, device=x.device, dtype=x.dtype - )[:h] - arange = arange.unsqueeze(0).repeat(h, 1).unsqueeze(2) - base_grid = torch.cat([arange, arange.transpose(1, 0)], dim=2) - base_grid = base_grid.unsqueeze(0).repeat(n, 1, 1, 1) - - shift = torch.randint( - 0, 2 * self.pad + 1, size=(n, 1, 1, 2), device=x.device, dtype=x.dtype - ) - shift *= 2.0 / (h + 2 * self.pad) - - grid = base_grid + shift - return F.grid_sample(x, grid, padding_mode="zeros", align_corners=False) From 4f598bd9947bb76a10e81cac018ede4f930d3b6f Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 22 Oct 2024 20:19:25 +0000 Subject: [PATCH 10/39] Made requested fixes, made bugfix to frame stack wrapper --- .../diffusion_policy/make_env.py | 17 +++++++---------- mani_skill/envs/tasks/drawing/draw_triangle.py | 12 ++++++------ .../panda_stick/solutions/draw_triangle.py | 17 +++++++++++++++-- mani_skill/utils/wrappers/flatten.py | 1 - mani_skill/utils/wrappers/frame_stack.py | 11 +++++++++++ 5 files changed, 39 insertions(+), 19 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index 0a210d79f..d18f5270c 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -12,7 +12,7 @@ from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv -class DictFrameStack(GymFrameStack): +class DictFrameStack(FrameStack): def __init__( self, env: gym.Env, @@ -26,15 +26,11 @@ def __init__( num_stack (int): The number of frames to stack lz4_compress (bool): Use lz4 to compress the frames internally """ - gym.utils.RecordConstructorArgs.__init__( - self, num_stack=num_stack, lz4_compress=lz4_compress - ) - gym.ObservationWrapper.__init__(self, env) - - self.num_stack = num_stack - self.lz4_compress = lz4_compress - - self.frames = deque(maxlen=num_stack) + # gym.utils.RecordConstructorArgs.__init__( + # self, num_stack=num_stack, lz4_compress=lz4_compress + # ) + # gym.ObservationWrapper.__init__(self, env) + super().__init__(env, num_stack, lz4_compress) new_observation_space = gym.spaces.Dict() for k, v in self.observation_space.items(): @@ -43,6 +39,7 @@ def __init__( new_observation_space[k] = Box(low=low, high=high, dtype=v.dtype) self.observation_space = new_observation_space + def observation(self, observation): """Converts the wrappers current frames to lazy frames. diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 38b3fc365..c7b35b753 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -28,7 +28,7 @@ class DrawTriangle(BaseEnv): to create their own drawing type tasks. """ - MAX_DOTS = 1010 + MAX_DOTS = 300 """ The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering when too many objects are being rendered in many scenes. @@ -102,26 +102,26 @@ def create_goal_triangle(name="tri", base_color=None): box1_half_w = 0.3 / 2 box1_half_h = 0.01 / 2 - half_thickness = 0.04 / 2 + half_thickness = 0.001 / 2 radius = (box1_half_w) / math.sqrt(3) theta = np.pi / 2 # define centers and compute verticies, might need to adjust how centers are calculated or add a theta arg for variation - c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.001]) + c1 = np.array([radius * math.cos(theta), radius * math.sin(theta), 0.01]) c2 = np.array( [ radius * math.cos(theta + (2 * np.pi / 3)), radius * math.sin(theta + (2 * np.pi / 3)), - 0.001, + 0.01, ] ) c3 = np.array( [ radius * math.cos((theta + (4 * np.pi / 3))), radius * math.sin(theta + (4 * np.pi / 3)), - 0.001, + 0.01, ] ) self.original_verts = np.array( @@ -212,7 +212,7 @@ def create_goal_triangle(name="tri", base_color=None): self.dots.append(actor) self.goal_tri = create_goal_triangle( name="goal_tri", - base_color=np.array([50, 50, 50, 200]) / 255, + base_color=np.array([50, 50, 50, 1]) / 255, ) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py index fbabb3f83..18cbdc594 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py @@ -1,9 +1,7 @@ -import numpy as np import sapien from mani_skill.envs.tasks import PushCubeEnv from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ PandaStickMotionPlanningSolver -from PIL import Image def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): @@ -15,24 +13,39 @@ def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): base_pose=env.unwrapped.agent.robot.pose, visualize_target_grasp_pose=vis, print_env_info=False, + joint_vel_limits = 0.3 ) FINGER_LENGTH = 0.025 env = env.unwrapped rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) + + # -------------------------------------------------------------------------- # + # Move to first vertex + # -------------------------------------------------------------------------- # + reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot) res = planner.move_to_pose_with_screw(reach_pose) + # -------------------------------------------------------------------------- # # Move to second vertex # -------------------------------------------------------------------------- # reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) + + # -------------------------------------------------------------------------- # + # Move to third vertex + # -------------------------------------------------------------------------- # reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) + # -------------------------------------------------------------------------- # + # Move back to first vertex + # -------------------------------------------------------------------------- # + reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot) res = planner.move_to_pose_with_screw(reach_pose) diff --git a/mani_skill/utils/wrappers/flatten.py b/mani_skill/utils/wrappers/flatten.py index 126dcbe82..989b95125 100644 --- a/mani_skill/utils/wrappers/flatten.py +++ b/mani_skill/utils/wrappers/flatten.py @@ -51,7 +51,6 @@ def observation(self, observation: Dict): if self.include_rgb and not self.include_depth: ret["rgb"] = images elif self.include_rgb and self.include_depth: - # todo: might not be the best way or the right place to perform this if self.sep_depth: ret["rgb"] = images[...,:-1] ret["depth"] = images[...,-1:] diff --git a/mani_skill/utils/wrappers/frame_stack.py b/mani_skill/utils/wrappers/frame_stack.py index 3a3e5c4d2..d8242abca 100644 --- a/mani_skill/utils/wrappers/frame_stack.py +++ b/mani_skill/utils/wrappers/frame_stack.py @@ -76,6 +76,15 @@ def __getitem__(self, int_or_slice: Union[int, slice]): [self._check_decompress(f) for f in self._frames[int_or_slice]], axis=0 ) + def _check_decompress(self, frame): + if self.lz4_compress: + from lz4.block import decompress + + return np.frombuffer(decompress(frame), dtype=self.dtype).reshape( + self.frame_shape + ) + return frame + def __eq__(self, other): """Checks that the current frames are equal to the other object.""" return self.__array__() == other @@ -98,6 +107,7 @@ def __init__( self, env: gym.Env, num_stack: int, + lz4_compress: bool = False ): """Observation wrapper that stacks the observations in a rolling manner. @@ -110,6 +120,7 @@ def __init__( self.num_stack = num_stack self.frames = deque(maxlen=num_stack) + self.lz4_compress = lz4_compress [self.frames.append(self.base_env._init_raw_obs) for _ in range(self.num_stack)] new_obs = self.observation(self.base_env._init_raw_obs) self.base_env.update_obs_space(new_obs) From 7a52287e73eb008540355d4a246dae254b775e4c Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 22 Oct 2024 22:32:19 +0000 Subject: [PATCH 11/39] Edited make_env and frame_stack --- .../baselines/diffusion_policy/diffusion_policy/make_env.py | 3 +-- mani_skill/utils/wrappers/frame_stack.py | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py index d18f5270c..f8543185a 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/make_env.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/make_env.py @@ -5,12 +5,11 @@ import mani_skill.envs import numpy as np from gymnasium.spaces import Box -from gymnasium.wrappers.frame_stack import FrameStack as GymFrameStack from gymnasium.wrappers.frame_stack import LazyFrames from mani_skill.utils import gym_utils from mani_skill.utils.wrappers import CPUGymWrapper, FrameStack, RecordEpisode from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv - +# from mani_skill.utils.wrappers.frame_stack import LazyFrames class DictFrameStack(FrameStack): def __init__( diff --git a/mani_skill/utils/wrappers/frame_stack.py b/mani_skill/utils/wrappers/frame_stack.py index d8242abca..fb0aaf9ce 100644 --- a/mani_skill/utils/wrappers/frame_stack.py +++ b/mani_skill/utils/wrappers/frame_stack.py @@ -79,8 +79,7 @@ def __getitem__(self, int_or_slice: Union[int, slice]): def _check_decompress(self, frame): if self.lz4_compress: from lz4.block import decompress - - return np.frombuffer(decompress(frame), dtype=self.dtype).reshape( + return torch.frombuffer(decompress(frame), dtype=self.dtype).reshape( self.frame_shape ) return frame From 7adcd5dee72e2e0df653e49318b2490bc25adb71 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 24 Oct 2024 19:17:01 +0000 Subject: [PATCH 12/39] Added state obs to draw triangle --- examples/baselines/diffusion_policy/examples.sh | 14 +++++++++++++- mani_skill/envs/tasks/drawing/draw_triangle.py | 14 ++++++++++++-- 2 files changed, 25 insertions(+), 3 deletions(-) diff --git a/examples/baselines/diffusion_policy/examples.sh b/examples/baselines/diffusion_policy/examples.sh index d59255b05..5739b2851 100644 --- a/examples/baselines/diffusion_policy/examples.sh +++ b/examples/baselines/diffusion_policy/examples.sh @@ -44,4 +44,16 @@ python -m mani_skill.trajectory.replay_trajectory \ python train.py --env-id PegInsertionSide-v1 \ --demo-path ~/.maniskill/demos/PegInsertionSide-v1/motionplanning/trajectory.state.pd_ee_delta_pose.cpu.h5 \ --control-mode "pd_ee_delta_pose" --sim-backend "cpu" --num-demos 100 --max_episode_steps 300 \ - --total_iters 300000 \ No newline at end of file + --total_iters 300000 + +# DrawTriangle-v1 +python -m mani_skill.trajectory.replay_trajectory \ + --traj-path ~/.maniskill/demos/DrawTriangle-v1/motionplanning/trajectory.h5 \ + --use-first-env-state -c pd_ee_delta_pose -o state \ + --save-traj --num-procs 10 -b cpu + +# RGBD based observations +python train_rgbd.py --env-id DrawTriangle-v1 \ + --demo-path ~/.maniskill/demos/DrawTriangle-v1/motionplanning/trajectory.rgbd.pd_ee_delta_pose.cpu.h5 + --control-mode "pd_ee_delta_pose" --sim-backend "cpu" --num-demos 100 --max_episode_steps 300 \ + --total-iters 300000 \ No newline at end of file diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index c7b35b753..9547e561e 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -212,7 +212,7 @@ def create_goal_triangle(name="tri", base_color=None): self.dots.append(actor) self.goal_tri = create_goal_triangle( name="goal_tri", - base_color=np.array([50, 50, 50, 1]) / 255, + base_color=np.array([10, 10, 10,255]) / 255, ) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): @@ -293,10 +293,20 @@ def evaluate(self): return {"success": out} def _get_obs_extra(self, info: Dict): - return dict( + obs = dict( tcp_pose=self.agent.tcp.pose.raw_pose, ) + if "state" in self.obs_mode: + obs.update( + goal_pose = self.goal_tri.pose.raw_pose, + tcp_to_verts_pos = self.vertices - self.agent.tcp.pose.p.unsqueeze(1), + goal_pos=self.goal_tri.pose.p, + vertices = self.vertices + ) + + return obs + def generate_triangle_with_points(self, n, vertices): batch_size = vertices.shape[0] From a209f59a7c61fa900fc7db4ac47be0b7aa8f2b23 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Thu, 24 Oct 2024 12:21:12 -0700 Subject: [PATCH 13/39] Update draw_triangle max steps --- mani_skill/envs/tasks/drawing/draw_triangle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 9547e561e..4353dddf6 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -21,7 +21,7 @@ from transforms3d.quaternions import quat2mat -@register_env("DrawTriangle-v1", max_episode_steps=1000) +@register_env("DrawTriangle-v1", max_episode_steps=300) class DrawTriangle(BaseEnv): """ This is a simple environment demonstrating drawing simulation on a table with a robot arm. There are no success/rewards defined, users can use this code as a starting point From b5528d6d20db217762db0ab513caf4652869f603 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Sat, 26 Oct 2024 02:15:03 +0000 Subject: [PATCH 14/39] Added DrawTriangle Docs --- docs/source/tasks/drawing/index.md | 23 ++++++++++++++++++ .../environment_demos/DrawTriangle-v1_rt.mp4 | Bin 0 -> 410912 bytes .../envs/tasks/drawing/draw_triangle.py | 5 ---- 3 files changed, 23 insertions(+), 5 deletions(-) create mode 100644 figures/environment_demos/DrawTriangle-v1_rt.mp4 diff --git a/docs/source/tasks/drawing/index.md b/docs/source/tasks/drawing/index.md index daf03fe02..ad304f349 100644 --- a/docs/source/tasks/drawing/index.md +++ b/docs/source/tasks/drawing/index.md @@ -26,3 +26,26 @@ None + +## DrawTriangle-v1 + +:::{dropdown} Task Card +:icon: note +:color: primary + +**Task Description:** +Instantiates a table with a white canvas on it and a goal triangle with an outline. 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z$N}J9fE56r04N*OM-gB~z^4MT1y~s1M}YqZXcp+-0?Y^aIY4lKw*U;!3+C|9On`d; zP6IsLK4=SiBEayr!ZAokgV!$!@L_lbp)f#TzJ~^m0c7Aa zs4pO(hxQC$xNrLb0pFp4xiAv&G4v-O;0rWmfI+*!bQA*p4E$vbFwjE)bw=0(_Wb|` z*ByWKLHjV4fjt|L1}D%?%szlY`A9&9qz&*3fD3>=NQdqM4BCf01Z3bCKw<{@7Xb$4 zBh>;*2G=vMK$-S{7X)MmKE(m*g&YESGLXLuTzfD8tONK4KtPUx0%U);9U2ZWIKhCo z9{>0QaShm-0=xm(x1d}EaJ(Rc`a*F4J_B?K+CvQV6+qepfPp?5{CI%3-wKc@!2AFc z0UjL?*q3Oa40wM*ECToi*gs%Oj|6X@5Fq&Rm<{j|;DZ1a1AHG~a2!J40eS@T2Likf zc=$es0SxjW^#dN{gZuwpFQ_BDJitR80bc>+U>`z3IZ!r0a*#iBbyMJ@KLG)3*#Vij znz Date: Mon, 28 Oct 2024 11:46:24 -0700 Subject: [PATCH 15/39] Fixed naming --- mani_skill/envs/tasks/drawing/draw_triangle.py | 2 +- .../motionplanning/panda_stick/solutions/draw_triangle.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 9bd1c10f0..bf9732ef1 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -22,7 +22,7 @@ @register_env("DrawTriangle-v1", max_episode_steps=300) -class DrawTriangle(BaseEnv): +class DrawTriangleEnv(BaseEnv): MAX_DOTS = 300 """ The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py index 18cbdc594..a9259d73b 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_triangle.py @@ -1,10 +1,10 @@ import sapien -from mani_skill.envs.tasks import PushCubeEnv +from mani_skill.envs.tasks import DrawTriangleEnv from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ PandaStickMotionPlanningSolver -def solve(env: PushCubeEnv, seed=None, debug=False, vis=False): +def solve(env: DrawTriangleEnv, seed=None, debug=False, vis=False): env.reset(seed=seed) planner = PandaStickMotionPlanningSolver( env, From 3c0bcae57a00c12d6d99fdd6d8168a4afba672da Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 29 Oct 2024 21:39:12 +0000 Subject: [PATCH 16/39] draw svg progress --- mani_skill/envs/tasks/drawing/__init__.py | 3 +- mani_skill/envs/tasks/drawing/draw_svg.py | 365 ++++++++++++++++++ .../motionplanning/panda_stick/run.py | 7 +- .../panda_stick/solutions/__init__.py | 1 + .../panda_stick/solutions/draw_svg.py | 60 +++ 5 files changed, 432 insertions(+), 4 deletions(-) create mode 100644 mani_skill/envs/tasks/drawing/draw_svg.py create mode 100644 mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py diff --git a/mani_skill/envs/tasks/drawing/__init__.py b/mani_skill/envs/tasks/drawing/__init__.py index f90b9a236..7603c783f 100644 --- a/mani_skill/envs/tasks/drawing/__init__.py +++ b/mani_skill/envs/tasks/drawing/__init__.py @@ -1,2 +1,3 @@ from .draw import * -from .draw_triangle import * \ No newline at end of file +from .draw_triangle import * +from .draw_svg import * \ No newline at end of file diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py new file mode 100644 index 000000000..a32c86276 --- /dev/null +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -0,0 +1,365 @@ +import math +import random +from typing import Dict + + + + +import mani_skill.envs.utils.randomization as randomization +import numpy as np +import sapien +import torch +from mani_skill.agents.robots.panda.panda_stick import PandaStick +from mani_skill.envs.sapien_env import BaseEnv +from mani_skill.sensors.camera import CameraConfig +from mani_skill.utils import sapien_utils +from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix +from mani_skill.utils.registration import register_env +from mani_skill.utils.scene_builder.table.scene_builder import \ + TableSceneBuilder +from mani_skill.utils.structs.actor import Actor +from mani_skill.utils.structs.pose import Pose +from mani_skill.utils.structs.types import SceneConfig, SimConfig +from transforms3d.euler import euler2quat, quat2euler +from transforms3d.quaternions import quat2mat + +import svgpathtools +from svgpathtools import Line, QuadraticBezier, CubicBezier + +@register_env("DrawSVG-v1", max_episode_steps=300) +class DrawSVG(BaseEnv): + + MAX_DOTS = 1000 + """ + The total "ink" available to use and draw with before you need to call env.reset. NOTE that on GPU simulation it is not recommended to have a very high value for this as it can slow down rendering + when too many objects are being rendered in many scenes. + """ + DOT_THICKNESS = 0.003 + """thickness of the paint drawn on to the canvas""" + CANVAS_THICKNESS = 0.02 + """How thick the canvas on the table is""" + BRUSH_RADIUS = 0.01 + """The brushes radius""" + BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]] + """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" + THRESHOLD = 0.05 + + SUPPORTED_REWARD_MODES = ["sparse"] + + SUPPORTED_ROBOTS: ["panda_stick"] # type: ignore + agent: PandaStick + + def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): + if svg == None: + self.svg = """M 100,30 + C 60,30 30,60 30,100 + C 30,140 60,170 100,190 + C 140,170 170,140 170,100 + C 170,60 140,30 100,30""" + else: + self.svg = svg + + super().__init__(*args, robot_uids=robot_uids, **kwargs) + + @property + def _default_sim_config(self): + # we set contact_offset to a small value as we are not expecting to make any contacts really apart from the brush hitting the canvas too hard. + # We set solver iterations very low as this environment is not doing a ton of manipulation (the brush is attached to the robot after all) + return SimConfig( + sim_freq=100, + control_freq=20, + scene_config=SceneConfig( + contact_offset=0.01, + solver_position_iterations=4, + solver_velocity_iterations=0, + ), + ) + + @property + def _default_sensor_configs(self): + pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1]) + return [ + CameraConfig( + "base_camera", + pose=pose, + width=320, + height=240, + fov=1.2, + near=0.01, + far=100, + ) + ] + + @property + def _default_human_render_camera_configs(self): + pose = sapien_utils.look_at(eye=[0.3, 0, 0.8], target=[0, 0, 0.1]) + return CameraConfig( + "render_camera", + pose=pose, + width=1280, + height=960, + fov=1.2, + near=0.01, + far=100, + ) + + def _load_scene(self, options: dict): + + self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0) + self.table_scene.build() + + + + def bezier_points(points, num_points=10): + """ + Generate points along a quadratic or cubic Bezier curve using numpy. + + Args: + *points: Variable number of control points as (x,y) tuples: + - For quadratic: (start, control, end) + - For cubic: (start, control1, control2, end) + num_points: Number of points to generate along curve + + Returns: + Array of points along the curve, shape (num_points, 2) + """ + points = np.array(points) + + if len(points) not in [3, 4]: + raise ValueError("Must provide either 3 points (quadratic) or 4 points (cubic)") + t = np.linspace(0, 1, num_points).reshape(-1, 1) + + if len(points) == 3: + p0, p1, p2 = points + points = ( + (1 - t)**2 * p0 + + 2 * t * (1 - t) * p1 + + t**2 * p2 + ) + else: + p0, p1, p2, p3 = points + points = ( + (1 - t)**3 * p0 + + 3 * t * (1 - t)**2 * p1 + + 3 * t**2 * (1 - t) * p2 + + t**3 * p3 + ) + + return points + + parsed_svg = svgpathtools.parse_path(self.svg) + + if not parsed_svg.iscontinuous(): + raise ValueError("SVG path must be continuous") + lines = [] + for path in parsed_svg: + if isinstance(path,QuadraticBezier) or isinstance(path, CubicBezier): + pts = bezier_points([[p.real, p.imag] for p in path.bpoints()]) + for i in range(len(pts)-1): + lines.append([pts[i],pts[i+1]]) + if isinstance(path, Line): + lines.append([[p.real, p.imag] for p in path.bpoints()]) + lines = np.array(lines) + lines = (lines / np.max(lines)) * 0.3 + lines = np.concatenate([lines, np.ones((*lines.shape[:-1],1)) * 0.02], -1) + center = lines[:,:1,:].mean(axis=0) * np.array([[1,1,0]]) + lines = lines - center + self.original_points = np.concatenate((lines[:1,0],lines[:,1,:])) + + def create_goal_outline(name="svg", base_color=None): + midpoints = np.mean(lines, axis=1) + box_half_ws = np.linalg.norm(lines[:,1]-lines[:,0], axis=1) / 2 + + box_half_h = 0.01 / 2 + half_thickness = 0.001 / 2 + mids = midpoints[:,:2] + ends = lines[:,1,:2] # 36, 2 + vec = ends-mids + print(vec) + angles = np.arccos(vec.dot(np.array([0,-1])) / (box_half_ws*2)) + + builder = self.scene.create_actor_builder() + for i,m in enumerate(midpoints): + pose = sapien.Pose(p=m, q = euler2quat(0,0,angles[i])) + + builder.add_box_visual( + pose=pose, + half_size = [box_half_ws[i], box_half_h, half_thickness], # type: ignore + material=sapien.render.RenderMaterial( + base_color=base_color, + ), + ) + + + return builder.build_kinematic(name=name) + + # build a white canvas on the table + self.canvas = self.scene.create_actor_builder() + self.canvas.add_box_visual( + half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2], + material=sapien.render.RenderMaterial(base_color=[1, 1, 1, 1]), + ) + self.canvas.add_box_collision( + half_size=[0.4, 0.6, self.CANVAS_THICKNESS / 2], + ) + self.canvas.initial_pose = sapien.Pose(p=[-0.1, 0, self.CANVAS_THICKNESS / 2]) + self.canvas = self.canvas.build_static(name="canvas") + + self.dots = [] + self.dot_pos = None + color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,)) + for i in range(self.MAX_DOTS): + actors = [] + if len(self.BRUSH_COLORS) > 1: + for env_idx in range(self.num_envs): + builder = self.scene.create_actor_builder() + builder.add_cylinder_visual( + radius=self.BRUSH_RADIUS, + half_length=self.DOT_THICKNESS / 2, + material=sapien.render.RenderMaterial( + base_color=self.BRUSH_COLORS[color_choices[env_idx]] + ), + ) + builder.set_scene_idxs([env_idx]) + actor = builder.build_kinematic(name=f"dot_{i}_{env_idx}") + actors.append(actor) + self.dots.append(Actor.merge(actors)) + else: + builder = self.scene.create_actor_builder() + builder.add_cylinder_visual( + radius=self.BRUSH_RADIUS, + half_length=self.DOT_THICKNESS / 2, + material=sapien.render.RenderMaterial( + base_color=self.BRUSH_COLORS[0] + ), + ) + actor = builder.build_kinematic(name=f"dot_{i}") + self.dots.append(actor) + self.goal_tri = create_goal_outline( + name="goal_tri", + base_color=np.array([10, 10, 10,255]) / 255, + ) + + def _initialize_episode(self, env_idx: torch.Tensor, options: dict): + self.draw_step = 0 + with torch.device(self.device): + b = len(env_idx) + self.table_scene.initialize(env_idx) + # target_pos = torch.zeros((b, 3)) + + # target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1 + # target_pos[:, -1] = 0.01 + # qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) + # mats = quaternion_to_matrix(qs).to(self.device) + # self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + + # self.vertices = torch.from_numpy( + # np.tile(self.original_verts, (b, 1, 1)) + # ).to( + # self.device + # ) # b, 3, 3 + # self.vertices = ( + # mats.double() @ self.vertices.transpose(-1, -2).double() + # ).transpose( + # -1, -2 + # ) # apply rotation matrix + # self.vertices += target_pos.unsqueeze(1) + + # self.triangles = self.generate_triangle_with_points( + # 100, self.vertices[:, :, :-1] + # ) + + for dot in self.dots: + # initially spawn dots in the table so they aren't seen + dot.set_pose( + sapien.Pose( + p=[0, 0, -self.DOT_THICKNESS], q=euler2quat(0, np.pi / 2, 0) + ) + ) + + def _after_control_step(self): + if self.gpu_sim_enabled: + self.scene._gpu_fetch_all() + + # This is the actual, GPU parallelized, drawing code. + # This is not real drawing but seeks to mimic drawing by placing dots on the canvas whenever the robot is close enough to the canvas surface + # We do not actually check if the robot contacts the table (although that is possible) and instead use a fast method to check. + # We add a 0.005 meter of leeway to make it easier for the robot to get close to the canvas and start drawing instead of having to be super close to the table. + robot_touching_table = ( + self.agent.tcp.pose.p[:, 2] + < self.CANVAS_THICKNESS + self.DOT_THICKNESS + 0.005 + ) + robot_brush_pos = torch.zeros((self.num_envs, 3), device=self.device) + robot_brush_pos[:, 2] = -self.DOT_THICKNESS + robot_brush_pos[robot_touching_table, :2] = self.agent.tcp.pose.p[ + robot_touching_table, :2 + ] + robot_brush_pos[robot_touching_table, 2] = ( + self.DOT_THICKNESS / 2 + self.CANVAS_THICKNESS + ) + # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible + new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) + self.dots[self.draw_step].set_pose(new_dot_pos) + if new_dot_pos.get_p()[:, -1] > 0: + if self.dot_pos == None: + self.dot_pos = new_dot_pos.get_p()[:, None, :] + self.dot_pos = torch.cat( + (self.dot_pos, new_dot_pos.get_p()[:, None, :]), dim=1 + ) + + self.draw_step += 1 + + # on GPU sim we have to call _gpu_apply_all() to apply the changes we make to object poses. + if self.gpu_sim_enabled: + self.scene._gpu_apply_all() + + def evaluate(self): + # out = self.success_check() + return {"success": torch.zeros(self.num_envs).to(bool)} + + def _get_obs_extra(self, info: Dict): + obs = dict( + tcp_pose=self.agent.tcp.pose.raw_pose, + ) + + # if "state" in self.obs_mode: + # obs.update( + # goal_pose = self.goal_tri.pose.raw_pose, + # tcp_to_verts_pos = self.vertices - self.agent.tcp.pose.p.unsqueeze(1), + # goal_pos=self.goal_tri.pose.p, + # vertices = self.vertices + # ) + + return obs + + def generate_triangle_with_points(self, n, vertices): + batch_size = vertices.shape[0] + + all_points = [] + + for i in range(vertices.shape[1]): + start_vertex = vertices[:, i, :] + end_vertex = vertices[:, (i + 1) % vertices.shape[1], :] + t = torch.linspace(0, 1, n + 2, device=vertices.device)[:-1] + t = t.view(1, -1, 1).repeat(batch_size, 1, 2) + intermediate_points = ( + start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t + ) + all_points.append(intermediate_points) + all_points = torch.cat(all_points, dim=1) + + return all_points + + def success_check(self): + if self.dot_pos == None or len(self.dot_pos) == 0: + return torch.Tensor([False]).to(bool) + drawn_pts = self.dot_pos[:, :, :-1] + + distance_matrix = torch.sqrt( + torch.sum( + (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 + ) + ) + + Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD + return torch.Tensor([torch.all(Y_closeness)]).to(bool) \ No newline at end of file diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py index a0373cbfc..0d50def44 100644 --- a/mani_skill/examples/motionplanning/panda_stick/run.py +++ b/mani_skill/examples/motionplanning/panda_stick/run.py @@ -4,11 +4,11 @@ import gymnasium as gym import numpy as np from mani_skill.examples.motionplanning.panda_stick.solutions import \ - solveDrawTriangle + solveDrawTriangle, solveDrawSVG from mani_skill.utils.wrappers.record import RecordEpisode from tqdm import tqdm -MP_SOLUTIONS = {"DrawTriangle-v1": solveDrawTriangle} +MP_SOLUTIONS = {"DrawTriangle-v1": solveDrawTriangle, "DrawSVG-v1": solveDrawSVG} def parse_args(args=None): @@ -17,7 +17,7 @@ def parse_args(args=None): "-e", "--env-id", type=str, - default="DrawTriangle-v1", + default="DrawSVG-v1", help=f"Environment to run motion planning solver on. Available options are {list(MP_SOLUTIONS.keys())}", ) parser.add_argument( @@ -104,6 +104,7 @@ def main(args): source_desc="official motion planning solution from ManiSkill contributors", video_fps=30, save_on_reset=False, + record_reward=False ) solve = MP_SOLUTIONS[env_id] print(f"Motion Planning Running on {env_id}") diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py index 8ad0262c7..849429e97 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/__init__.py @@ -1 +1,2 @@ from .draw_triangle import solve as solveDrawTriangle +from .draw_svg import solve as solveDrawSVG \ No newline at end of file diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py new file mode 100644 index 000000000..983b5db33 --- /dev/null +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py @@ -0,0 +1,60 @@ + +import sapien + +from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ + PandaStickMotionPlanningSolver +from PIL import Image + + +def solve(env, seed=None, debug=False, vis=False): + env.reset(seed=seed) + planner = PandaStickMotionPlanningSolver( + env, + debug=debug, + vis=vis, + base_pose=env.unwrapped.agent.robot.pose, + visualize_target_grasp_pose=vis, + print_env_info=False, + ) + + FINGER_LENGTH = 0.025 + env = env.unwrapped + env.reset() + + + rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) + for point in env.original_points: + reach_pose = sapien.Pose(p=list(point), q=rot) + res = planner.move_to_pose_with_screw(reach_pose) + + + # # -------------------------------------------------------------------------- # + # # Move to first vertex + # # -------------------------------------------------------------------------- # + + # reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot) + # res = planner.move_to_pose_with_screw(reach_pose) + + # # -------------------------------------------------------------------------- # + # # Move to second vertex + # # -------------------------------------------------------------------------- # + + # reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot) + # res = planner.move_to_pose_with_screw(reach_pose) + + # # -------------------------------------------------------------------------- # + # # Move to third vertex + # # -------------------------------------------------------------------------- # + + # reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot) + # res = planner.move_to_pose_with_screw(reach_pose) + + # # -------------------------------------------------------------------------- # + # # Move back to first vertex + # # -------------------------------------------------------------------------- # + + # reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot) + # res = planner.move_to_pose_with_screw(reach_pose) + + planner.close() + return res \ No newline at end of file From 186677be27f3e521bdb92601c647b23ad90bb595 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Wed, 30 Oct 2024 08:36:45 +0000 Subject: [PATCH 17/39] fixed most issues and parallelized draw svg --- mani_skill/envs/tasks/drawing/draw_svg.py | 113 +++++++----------- .../panda_stick/solutions/draw_svg.py | 34 +----- 2 files changed, 47 insertions(+), 100 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index a32c86276..0aff102dd 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -51,11 +51,7 @@ class DrawSVG(BaseEnv): def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): if svg == None: - self.svg = """M 100,30 - C 60,30 30,60 30,100 - C 30,140 60,170 100,190 - C 140,170 170,140 170,100 - C 170,60 140,30 100,30""" + self.svg ="M316.9 18C311.6 7 300.4 0 288.1 0s-23.4 7-28.8 18L195 150.3 51.4 171.5c-12 1.8-22 10.2-25.7 21.7s-.7 24.2 7.9 32.7L137.8 329 113.2 474.7c-2 12 3 24.2 12.9 31.3s23 8 33.8 2.3l128.3-68.5 128.3 68.5c10.8 5.7 23.9 4.9 33.8-2.3s14.9-19.3 12.9-31.3L438.5 329 542.7 225.9c8.6-8.5 11.7-21.2 7.9-32.7s-13.7-19.9-25.7-21.7L381.2 150.3 316.9 18z" else: self.svg = svg @@ -110,7 +106,7 @@ def _load_scene(self, options: dict): - def bezier_points(points, num_points=10): + def bezier_points(points, num_points=5): """ Generate points along a quadratic or cubic Bezier curve using numpy. @@ -159,24 +155,26 @@ def bezier_points(points, num_points=10): lines.append([pts[i],pts[i+1]]) if isinstance(path, Line): lines.append([[p.real, p.imag] for p in path.bpoints()]) - lines = np.array(lines) - lines = (lines / np.max(lines)) * 0.3 - lines = np.concatenate([lines, np.ones((*lines.shape[:-1],1)) * 0.02], -1) - center = lines[:,:1,:].mean(axis=0) * np.array([[1,1,0]]) + lines = np.array(lines) # n, 2, 2 + lines = (lines / np.max(lines)) * 0.25 # scale the svg down to fit + lines = np.concatenate([lines, np.ones((*lines.shape[:-1],1)) * 0.01], -1) # b, 2, 3 + center = lines[:,:1,:].mean(axis=0) * np.array([[1,1,0]]) # calculate transform to be in range of arm lines = lines - center - self.original_points = np.concatenate((lines[:1,0],lines[:,1,:])) + self.original_points = np.concatenate((lines[:1,0],lines[:,1,:])) def create_goal_outline(name="svg", base_color=None): - midpoints = np.mean(lines, axis=1) + midpoints = np.mean(lines, axis=1) # midpoints of line segments box_half_ws = np.linalg.norm(lines[:,1]-lines[:,0], axis=1) / 2 box_half_h = 0.01 / 2 half_thickness = 0.001 / 2 mids = midpoints[:,:2] - ends = lines[:,1,:2] # 36, 2 - vec = ends-mids - print(vec) - angles = np.arccos(vec.dot(np.array([0,-1])) / (box_half_ws*2)) + ends = lines[:,1,:2] # n, 2 + + # calculate rot angles abt z axis + vec = ends-mids + angles = np.arctan2(vec[:,1], vec[:,0]) + builder = self.scene.create_actor_builder() for i,m in enumerate(midpoints): @@ -235,9 +233,9 @@ def create_goal_outline(name="svg", base_color=None): ) actor = builder.build_kinematic(name=f"dot_{i}") self.dots.append(actor) - self.goal_tri = create_goal_outline( + self.goal_outline = create_goal_outline( name="goal_tri", - base_color=np.array([10, 10, 10,255]) / 255, + base_color=np.array([10, 10, 10, 255]) / 255, ) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): @@ -245,29 +243,23 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): with torch.device(self.device): b = len(env_idx) self.table_scene.initialize(env_idx) - # target_pos = torch.zeros((b, 3)) - - # target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1 - # target_pos[:, -1] = 0.01 - # qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) - # mats = quaternion_to_matrix(qs).to(self.device) - # self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) - - # self.vertices = torch.from_numpy( - # np.tile(self.original_verts, (b, 1, 1)) - # ).to( - # self.device - # ) # b, 3, 3 - # self.vertices = ( - # mats.double() @ self.vertices.transpose(-1, -2).double() - # ).transpose( - # -1, -2 - # ) # apply rotation matrix - # self.vertices += target_pos.unsqueeze(1) - - # self.triangles = self.generate_triangle_with_points( - # 100, self.vertices[:, :, :-1] - # ) + target_pos = torch.zeros((b, 3)) + + target_pos[:, :2] = torch.rand((b, 2)) * 0.02 - 0.1 + target_pos[:, -1] = 0.01 + qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) + rot_mat = quaternion_to_matrix(qs).to(self.device) + self.goal_outline.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + + self.points = torch.from_numpy( + np.tile(self.original_points, (b, 1, 1)) + ).to( + self.device + ) # b, n, 3 + + self.points = (rot_mat.double() @ self.points.transpose(-1,-2).double()).transpose(-1, -2) # rotation matrix + target_pos[:, -1] = 0.01 + self.points += target_pos.unsqueeze(1) for dot in self.dots: # initially spawn dots in the table so they aren't seen @@ -332,34 +324,17 @@ def _get_obs_extra(self, info: Dict): return obs - def generate_triangle_with_points(self, n, vertices): - batch_size = vertices.shape[0] - all_points = [] - - for i in range(vertices.shape[1]): - start_vertex = vertices[:, i, :] - end_vertex = vertices[:, (i + 1) % vertices.shape[1], :] - t = torch.linspace(0, 1, n + 2, device=vertices.device)[:-1] - t = t.view(1, -1, 1).repeat(batch_size, 1, 2) - intermediate_points = ( - start_vertex.unsqueeze(1) * (1 - t) + end_vertex.unsqueeze(1) * t - ) - all_points.append(intermediate_points) - all_points = torch.cat(all_points, dim=1) + # def success_check(self): + # if self.dot_pos == None or len(self.dot_pos) == 0: + # return torch.Tensor([False]).to(bool) + # drawn_pts = self.dot_pos[:, :, :-1] - return all_points - - def success_check(self): - if self.dot_pos == None or len(self.dot_pos) == 0: - return torch.Tensor([False]).to(bool) - drawn_pts = self.dot_pos[:, :, :-1] - - distance_matrix = torch.sqrt( - torch.sum( - (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 - ) - ) + # distance_matrix = torch.sqrt( + # torch.sum( + # (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 + # ) + # ) - Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD - return torch.Tensor([torch.all(Y_closeness)]).to(bool) \ No newline at end of file + # Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD + # return torch.Tensor([torch.all(Y_closeness)]).to(bool) \ No newline at end of file diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py index 983b5db33..2086267da 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py @@ -23,38 +23,10 @@ def solve(env, seed=None, debug=False, vis=False): rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) - for point in env.original_points: - reach_pose = sapien.Pose(p=list(point), q=rot) + res = None + for point in env.points[0]: + reach_pose = sapien.Pose(p=list(point.cpu().numpy()), q=rot) res = planner.move_to_pose_with_screw(reach_pose) - - - # # -------------------------------------------------------------------------- # - # # Move to first vertex - # # -------------------------------------------------------------------------- # - - # reach_pose = sapien.Pose(p=list(env.vertices[0, 0].numpy()), q=rot) - # res = planner.move_to_pose_with_screw(reach_pose) - - # # -------------------------------------------------------------------------- # - # # Move to second vertex - # # -------------------------------------------------------------------------- # - - # reach_pose = sapien.Pose(p=list(env.vertices[0, 1]), q=rot) - # res = planner.move_to_pose_with_screw(reach_pose) - - # # -------------------------------------------------------------------------- # - # # Move to third vertex - # # -------------------------------------------------------------------------- # - - # reach_pose = sapien.Pose(p=list(env.vertices[0, 2]), q=rot) - # res = planner.move_to_pose_with_screw(reach_pose) - - # # -------------------------------------------------------------------------- # - # # Move back to first vertex - # # -------------------------------------------------------------------------- # - - # reach_pose = sapien.Pose(p=list(env.vertices[0, 0]), q=rot) - # res = planner.move_to_pose_with_screw(reach_pose) planner.close() return res \ No newline at end of file From 518a5c5b336a686d9e1de620e1b8824aacdf0355 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Wed, 30 Oct 2024 01:43:32 -0700 Subject: [PATCH 18/39] Update draw_svg.py --- mani_skill/envs/tasks/drawing/draw_svg.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 0aff102dd..8352b6a49 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -258,7 +258,6 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): ) # b, n, 3 self.points = (rot_mat.double() @ self.points.transpose(-1,-2).double()).transpose(-1, -2) # rotation matrix - target_pos[:, -1] = 0.01 self.points += target_pos.unsqueeze(1) for dot in self.dots: @@ -337,4 +336,4 @@ def _get_obs_extra(self, info: Dict): # ) # Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD - # return torch.Tensor([torch.all(Y_closeness)]).to(bool) \ No newline at end of file + # return torch.Tensor([torch.all(Y_closeness)]).to(bool) From c8231efbfb3b75f8d500e1948d66221fdd1ffc1d Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Wed, 30 Oct 2024 19:42:37 +0000 Subject: [PATCH 19/39] added success condition and state based obs --- mani_skill/envs/tasks/drawing/draw_svg.py | 40 +++++++++++------------ 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 8352b6a49..24a42664e 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -305,35 +305,35 @@ def _after_control_step(self): self.scene._gpu_apply_all() def evaluate(self): - # out = self.success_check() - return {"success": torch.zeros(self.num_envs).to(bool)} + out = self.success_check() + return {"success": out} def _get_obs_extra(self, info: Dict): obs = dict( tcp_pose=self.agent.tcp.pose.raw_pose, ) - # if "state" in self.obs_mode: - # obs.update( - # goal_pose = self.goal_tri.pose.raw_pose, - # tcp_to_verts_pos = self.vertices - self.agent.tcp.pose.p.unsqueeze(1), - # goal_pos=self.goal_tri.pose.p, - # vertices = self.vertices - # ) + if "state" in self.obs_mode: + obs.update( + goal_pose = self.goal_outline.pose.raw_pose, + tcp_to_verts_pos = self.points - self.agent.tcp.pose.p.unsqueeze(1), + goal_pos=self.goal_outline.pose.p, + vertices = self.points + ) return obs - # def success_check(self): - # if self.dot_pos == None or len(self.dot_pos) == 0: - # return torch.Tensor([False]).to(bool) - # drawn_pts = self.dot_pos[:, :, :-1] + def success_check(self): + if self.dot_pos == None or len(self.dot_pos) == 0: + return torch.Tensor([False]).to(bool) + drawn_pts = self.dot_pos[:, :, :-1] - # distance_matrix = torch.sqrt( - # torch.sum( - # (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 - # ) - # ) + distance_matrix = torch.sqrt( + torch.sum( + (drawn_pts[:, :, None, :] - self.points[:, None, :, :2]) ** 2, axis=-1 + ) + ) - # Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD - # return torch.Tensor([torch.all(Y_closeness)]).to(bool) + Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD + return torch.Tensor([torch.all(Y_closeness)]).to(bool) From a1516f0a3cf58c42f6c65410e7fa442b6634a2d8 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Wed, 30 Oct 2024 22:16:25 +0000 Subject: [PATCH 20/39] Added discontinuous paths for draw svg --- mani_skill/envs/tasks/drawing/draw_svg.py | 20 +++++++++---------- .../panda_stick/solutions/draw_svg.py | 6 +++++- 2 files changed, 15 insertions(+), 11 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 24a42664e..6a3cc15e4 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -2,9 +2,6 @@ import random from typing import Dict - - - import mani_skill.envs.utils.randomization as randomization import numpy as np import sapien @@ -20,8 +17,7 @@ from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig -from transforms3d.euler import euler2quat, quat2euler -from transforms3d.quaternions import quat2mat +from transforms3d.euler import euler2quat import svgpathtools from svgpathtools import Line, QuadraticBezier, CubicBezier @@ -51,7 +47,7 @@ class DrawSVG(BaseEnv): def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): if svg == None: - self.svg ="M316.9 18C311.6 7 300.4 0 288.1 0s-23.4 7-28.8 18L195 150.3 51.4 171.5c-12 1.8-22 10.2-25.7 21.7s-.7 24.2 7.9 32.7L137.8 329 113.2 474.7c-2 12 3 24.2 12.9 31.3s23 8 33.8 2.3l128.3-68.5 128.3 68.5c10.8 5.7 23.9 4.9 33.8-2.3s14.9-19.3 12.9-31.3L438.5 329 542.7 225.9c8.6-8.5 11.7-21.2 7.9-32.7s-13.7-19.9-25.7-21.7L381.2 150.3 316.9 18z" + self.svg ="""M88 0C74.7 0 64 10.7 64 24c0 38.9 23.4 59.4 39.1 73.1l1.1 1C120.5 112.3 128 119.9 128 136c0 13.3 10.7 24 24 24s24-10.7 24-24c0-38.9-23.4-59.4-39.1-73.1l-1.1-1C119.5 47.7 112 40.1 112 24c0-13.3-10.7-24-24-24zM32 192c-17.7 0-32 14.3-32 32L0 416c0 53 43 96 96 96l192 0c53 0 96-43 96-96l16 0c61.9 0 112-50.1 112-112s-50.1-112-112-112l-48 0L32 192zm352 64l16 0c26.5 0 48 21.5 48 48s-21.5 48-48 48l-16 0 0-96zM224 24c0-13.3-10.7-24-24-24s-24 10.7-24 24c0 38.9 23.4 59.4 39.1 73.1l1.1 1C232.5 112.3 240 119.9 240 136c0 13.3 10.7 24 24 24s24-10.7 24-24c0-38.9-23.4-59.4-39.1-73.1l-1.1-1C231.5 47.7 224 40.1 224 24z""" else: self.svg = svg @@ -145,8 +141,6 @@ def bezier_points(points, num_points=5): parsed_svg = svgpathtools.parse_path(self.svg) - if not parsed_svg.iscontinuous(): - raise ValueError("SVG path must be continuous") lines = [] for path in parsed_svg: if isinstance(path,QuadraticBezier) or isinstance(path, CubicBezier): @@ -155,12 +149,18 @@ def bezier_points(points, num_points=5): lines.append([pts[i],pts[i+1]]) if isinstance(path, Line): lines.append([[p.real, p.imag] for p in path.bpoints()]) - lines = np.array(lines) # n, 2, 2 + lines = np.array(lines) # n, 2, 2 lines = (lines / np.max(lines)) * 0.25 # scale the svg down to fit lines = np.concatenate([lines, np.ones((*lines.shape[:-1],1)) * 0.01], -1) # b, 2, 3 center = lines[:,:1,:].mean(axis=0) * np.array([[1,1,0]]) # calculate transform to be in range of arm lines = lines - center - self.original_points = np.concatenate((lines[:1,0],lines[:,1,:])) + if not parsed_svg.iscontinuous(): + + disconts = lines[1:,0] -lines[:-1,1] + self.disconts = list(np.nonzero(np.logical_or(disconts[:,0], disconts[:,1]))[0]) # indices of where the discontinuities are ie. [1,]: discont betw ind 1 and 2 + self.continuous = False + + self.original_points = np.concatenate((lines[:1,0],lines[:,1,:])) def create_goal_outline(name="svg", base_color=None): midpoints = np.mean(lines, axis=1) # midpoints of line segments diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py index 2086267da..82f9b850a 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py @@ -1,5 +1,6 @@ import sapien +import numpy as np from mani_skill.examples.motionplanning.panda_stick.motionplanner import \ PandaStickMotionPlanningSolver @@ -24,9 +25,12 @@ def solve(env, seed=None, debug=False, vis=False): rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) res = None - for point in env.points[0]: + for i, point in enumerate(env.points[0]): reach_pose = sapien.Pose(p=list(point.cpu().numpy()), q=rot) res = planner.move_to_pose_with_screw(reach_pose) + + if i-1 in env.disconts: + res = planner.move_to_pose_with_screw(sapien.Pose(p=list(point.cpu().numpy() + np.array([0,0,0.1])), q=rot)) planner.close() return res \ No newline at end of file From 35aff3be5473908cf35a2465b04956aca2fb857e Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 31 Oct 2024 07:46:51 +0000 Subject: [PATCH 21/39] formatting, discontinuous state --- docs/source/tasks/drawing/index.md | 23 ++++ figures/environment_demos/DrawSVG-v1_rt.mp4 | Bin 0 -> 820995 bytes mani_skill/envs/tasks/drawing/draw_svg.py | 115 +++++++++--------- .../envs/tasks/drawing/draw_triangle.py | 8 +- .../motionplanning/panda_stick/run.py | 8 +- .../panda_stick/solutions/__init__.py | 2 +- .../panda_stick/solutions/draw_svg.py | 19 ++- .../panda_stick/solutions/draw_triangle.py | 4 +- 8 files changed, 101 insertions(+), 78 deletions(-) create mode 100644 figures/environment_demos/DrawSVG-v1_rt.mp4 diff --git a/docs/source/tasks/drawing/index.md b/docs/source/tasks/drawing/index.md index ad304f349..a62b00448 100644 --- a/docs/source/tasks/drawing/index.md +++ b/docs/source/tasks/drawing/index.md @@ -48,4 +48,27 @@ Instantiates a table with a white canvas on it and a goal triangle with an outli + +## DrawSVG-v1 + +:::{dropdown} Task Card +:icon: note +:color: primary + +**Task Description:** +Instantiates a table with a white canvas on it and a svg path specified with an outline. 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z$iyIuE0V;L8v6C3-r^D+fo4?{xML+d7*sHv2-|IpZafW=fmG+8yRMw}W z4q|&$?vKj*|cd%<#}6izEqyK6?G8X_rvqn=p+4oohtWDMV;Q)LzL;m zy8YSfQ+ZDPwbtv;zMuZ?{60Ph|L*hsf1jV>T(8U9@c_fgZaCY;`7Yl3iq<#|+k|&E z(RD>qbgU~$rbl@L-2(k{+TQ3h_~9M#K-rIH5IuNr97il)h*H-D zb3Zi`ur~$|K_F3mI$xXm0{j{A{PI?_iL~Vi@5^~uAsG$h-Y9FEH?b*eE6P6 zh|x4cKMj6F%lIbnSKR>JPq~c#c8o99o273uu%p(d}M?PQlYY6Klc$u+I;;b_48^LD;Iy}U@#QaQ{li{8f zd&`J?oyqeA`4K)Y=$Z7lLEnhv;7`p}9qDsJzF|D}jjAcw6g%r})QpxID>fnM#`wt8 zS~&?Jhsk_&cq@x&SyzpyjS2b^{HdMV8Vq*@apLvFxI6X)`kWBASc3wD`YV{P5jB^! z=$(*VZ1c9 zXvM`nCJ`@<8mEo+r}Ycd8Csz9>4(qndFCJG9~l`c4DYWN!4)48G~5^+ArjWp`Wu2p UQmL1JOkkUKjxOyTot>Qi1OChG8vp Date: Thu, 31 Oct 2024 07:48:33 +0000 Subject: [PATCH 22/39] Updated run.py --- mani_skill/examples/motionplanning/panda_stick/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mani_skill/examples/motionplanning/panda_stick/run.py b/mani_skill/examples/motionplanning/panda_stick/run.py index 3281da71c..10118b9ca 100644 --- a/mani_skill/examples/motionplanning/panda_stick/run.py +++ b/mani_skill/examples/motionplanning/panda_stick/run.py @@ -104,7 +104,6 @@ def main(args): source_desc="official motion planning solution from ManiSkill contributors", video_fps=30, save_on_reset=False, - record_reward=False, ) solve = MP_SOLUTIONS[env_id] print(f"Motion Planning Running on {env_id}") From 578105e862e9cf82b2a8acc73e92a274de6fc15b Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 4 Nov 2024 12:47:51 -0800 Subject: [PATCH 23/39] fixed state obs error for drawing envs --- mani_skill/envs/tasks/drawing/draw_svg.py | 10 +++++----- mani_skill/envs/tasks/drawing/draw_triangle.py | 8 ++++---- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 5c534f264..a8245900c 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -316,11 +316,11 @@ def _get_obs_extra(self, info: Dict): if "state" in self.obs_mode: obs.update( - goal_pose=self.goal_outline.pose.raw_pose, - tcp_to_verts_pos=self.points - self.agent.tcp.pose.p.unsqueeze(1), - goal_pos=self.goal_outline.pose.p, - vertices=self.points, - continuous=self.continuous, # if the path is continuous + goal_pose=self.goal_outline.pose.raw_pose.reshape(self.num_envs, -1), + tcp_to_verts_pos=(self.points - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), + goal_pos=(self.goal_outline.pose.p).reshape(self.num_envs, -1), + vertices=self.points.reshape(self.num_envs, -1), + continuous=self.continuous.reshape(self.num_envs, -1), # if the path is continuous ) return obs diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 4d1fbbcff..36db1357f 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -294,10 +294,10 @@ def _get_obs_extra(self, info: Dict): if "state" in self.obs_mode: obs.update( - goal_pose=self.goal_tri.pose.raw_pose, - tcp_to_verts_pos=self.vertices - self.agent.tcp.pose.p.unsqueeze(1), - goal_pos=self.goal_tri.pose.p, - vertices=self.vertices, + goal_pose=self.goal_tri.pose.raw_pose.reshape(self.num_envs, -1), + tcp_to_verts_pos=(self.vertices - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), + goal_pos=self.goal_tri.pose.p.reshape(self.num_envs, -1), + vertices=self.vertices.reshape(self.num_envs, -1), ) return obs From cac38a739cc97d09b2dccfebef587f8c9e796f93 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Thu, 7 Nov 2024 15:35:21 -0800 Subject: [PATCH 24/39] Changed drawsvg imports --- mani_skill/envs/tasks/drawing/draw_svg.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index a8245900c..d4089fbb0 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -16,7 +16,6 @@ from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig -from svgpathtools import CubicBezier, Line, QuadraticBezier from transforms3d.euler import euler2quat @@ -44,6 +43,9 @@ class DrawSVGEnv(BaseEnv): agent: PandaStick def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): + import svgpathtools + from svgpathtools import CubicBezier, Line, QuadraticBezier + if svg == None: self.svg = "M7.875 0L0 7.875V55.125L7.875 63H23.763L23.7235 62.9292L11.8418 51.2859L11.8418 35.6268L21.1302 26.915L23.9193 11.6649L40.9773 6.3631L46.8835 16.5929L33.2356 19.926L32.6417 29.1349L41.1407 33.618L50.8511 23.465L56.6781 33.5577L43.5576 45.6794L28.9369 40.4365L26.1844 42.4266L26.1844 45.6794L43.2157 63H55.125L63 55.125V7.875L55.125 0H7.875Z" self.continuous = True From ba99b50762175effb71bf9c24194b1547958a159 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Thu, 7 Nov 2024 15:36:15 -0800 Subject: [PATCH 25/39] Update draw_svg.py --- mani_skill/envs/tasks/drawing/draw_svg.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index d4089fbb0..b2c77f398 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -3,7 +3,6 @@ import mani_skill.envs.utils.randomization as randomization import numpy as np import sapien -import svgpathtools import torch from mani_skill.agents.robots.panda.panda_stick import PandaStick from mani_skill.envs.sapien_env import BaseEnv From 107f6f5b682bf4d542ac1d6e9788f6a2d31ae76d Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Fri, 8 Nov 2024 15:29:36 -0800 Subject: [PATCH 26/39] Update draw_svg.py --- mani_skill/envs/tasks/drawing/draw_svg.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index b2c77f398..c74917bc7 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -42,8 +42,6 @@ class DrawSVGEnv(BaseEnv): agent: PandaStick def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): - import svgpathtools - from svgpathtools import CubicBezier, Line, QuadraticBezier if svg == None: self.svg = "M7.875 0L0 7.875V55.125L7.875 63H23.763L23.7235 62.9292L11.8418 51.2859L11.8418 35.6268L21.1302 26.915L23.9193 11.6649L40.9773 6.3631L46.8835 16.5929L33.2356 19.926L32.6417 29.1349L41.1407 33.618L50.8511 23.465L56.6781 33.5577L43.5576 45.6794L28.9369 40.4365L26.1844 42.4266L26.1844 45.6794L43.2157 63H55.125L63 55.125V7.875L55.125 0H7.875Z" @@ -96,7 +94,12 @@ def _default_human_render_camera_configs(self): ) def _load_scene(self, options: dict): - + try: + import svgpathtools + from svgpathtools import CubicBezier, Line, QuadraticBezier + except ImportError: + raise ImportError("svgpathtools not installed. Install with pip install svgpathtools") + self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0) self.table_scene.build() From d2414194153063c1879272a82c93a68f2e570805 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Fri, 8 Nov 2024 18:52:28 -0800 Subject: [PATCH 27/39] Small bugfix --- mani_skill/envs/tasks/drawing/draw_svg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index c74917bc7..785f27de2 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -324,7 +324,7 @@ def _get_obs_extra(self, info: Dict): tcp_to_verts_pos=(self.points - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), goal_pos=(self.goal_outline.pose.p).reshape(self.num_envs, -1), vertices=self.points.reshape(self.num_envs, -1), - continuous=self.continuous.reshape(self.num_envs, -1), # if the path is continuous + continuous=torch.ones((self.num_envs, 1)) * self.continuous # if the path is continuous ) return obs From b7fe6fea3cf15d9cdb54aad7a1597f390fb3e375 Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Sat, 9 Nov 2024 16:21:44 -0800 Subject: [PATCH 28/39] Update draw_svg.py --- .../examples/motionplanning/panda_stick/solutions/draw_svg.py | 1 - 1 file changed, 1 deletion(-) diff --git a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py index 190760e63..7297a7fbd 100644 --- a/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py +++ b/mani_skill/examples/motionplanning/panda_stick/solutions/draw_svg.py @@ -18,7 +18,6 @@ def solve(env: DrawSVGEnv, seed=None, debug=False, vis=False): FINGER_LENGTH = 0.025 env = env.unwrapped - env.reset() rot = list(env.agent.tcp.pose.get_q()[0].cpu().numpy()) res = None From aa4de17807551a46dac2b8d81032c176909f073b Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Sun, 10 Nov 2024 16:46:46 -0800 Subject: [PATCH 29/39] Update draw_svg.py --- mani_skill/envs/tasks/drawing/draw_svg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 785f27de2..796174e16 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -324,7 +324,7 @@ def _get_obs_extra(self, info: Dict): tcp_to_verts_pos=(self.points - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), goal_pos=(self.goal_outline.pose.p).reshape(self.num_envs, -1), vertices=self.points.reshape(self.num_envs, -1), - continuous=torch.ones((self.num_envs, 1)) * self.continuous # if the path is continuous + continuous=torch.ones((self.num_envs, 1),device=self.device) * self.continuous # if the path is continuous ) return obs From 9a78a43679f96ea6bdcab281df5bf36e58d32c1a Mon Sep 17 00:00:00 2001 From: "Arnav G." <73305591+arnavg115@users.noreply.github.com> Date: Fri, 15 Nov 2024 01:26:45 -0800 Subject: [PATCH 30/39] Update draw_triangle.py --- .../envs/tasks/drawing/draw_triangle.py | 49 ++++++++++++------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 36db1357f..23e695008 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -176,7 +176,6 @@ def create_goal_triangle(name="tri", base_color=None): self.canvas = self.canvas.build_static(name="canvas") self.dots = [] - self.dot_pos = None color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,)) for i in range(self.MAX_DOTS): actors = [] @@ -213,7 +212,8 @@ def create_goal_triangle(name="tri", base_color=None): def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 with torch.device(self.device): - b = len(env_idx) + self.dot_pos = [] + b = self.num_envs self.table_scene.initialize(env_idx) target_pos = torch.zeros((b, 3)) @@ -228,6 +228,9 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): ).to( self.device ) # b, 3, 3 + print(self.original_verts) + print(self.vertices.shape) + self.vertices = ( mats.double() @ self.vertices.transpose(-1, -2).double() ).transpose( @@ -268,15 +271,10 @@ def _after_control_step(self): self.DOT_THICKNESS / 2 + self.CANVAS_THICKNESS ) # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible + self.dot_pos.append(robot_brush_pos) new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) self.dots[self.draw_step].set_pose(new_dot_pos) - if new_dot_pos.get_p()[:, -1] > 0: - if self.dot_pos == None: - self.dot_pos = new_dot_pos.get_p()[:, None, :] - self.dot_pos = torch.cat( - (self.dot_pos, new_dot_pos.get_p()[:, None, :]), dim=1 - ) - + self.draw_step += 1 # on GPU sim we have to call _gpu_apply_all() to apply the changes we make to object poses. @@ -321,15 +319,28 @@ def generate_triangle_with_points(self, n, vertices): return all_points def success_check(self): - if self.dot_pos == None or len(self.dot_pos) == 0: - return torch.Tensor([False]).to(bool) - drawn_pts = self.dot_pos[:, :, :-1] - distance_matrix = torch.sqrt( - torch.sum( - (drawn_pts[:, :, None, :] - self.triangles[:, None, :, :]) ** 2, axis=-1 - ) - ) + if len(self.dot_pos) == 0: + return torch.zeros((self.num_envs), device=self.device).to(bool) + + dot_pos = torch.vstack(self.dot_pos).reshape(self.num_envs, -1, 3) + positive_z_mask = dot_pos[:, :, 2] > 0 + + batch_size = dot_pos.shape[0] + result = torch.zeros(batch_size, dtype=torch.bool, device=self.device) + + for b in range(batch_size): + valid_points = dot_pos[b][positive_z_mask[b]][...,:2] # shape: [num_valid_in_batch, 3] + if len(valid_points) > 0: + distance_matrix = torch.sqrt( + torch.sum( + (valid_points[:, None, :] - self.triangles[b]) ** 2, + dim=-1 + ) + ) + min_distances = torch.min(distance_matrix, dim=1).values # shape: [num_valid_in_batch] + result[b] = torch.all(min_distances < self.THRESHOLD) + else: + result[b] = False - Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD - return torch.Tensor([torch.all(Y_closeness)]).to(bool) + return result From 78b2a491ea028c3c241cd839e5926da40317ab5d Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Sat, 16 Nov 2024 00:25:03 +0000 Subject: [PATCH 31/39] Bugfixes, speed progress --- .../envs/tasks/drawing/draw_triangle.py | 46 +++++++++++-------- 1 file changed, 28 insertions(+), 18 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 23e695008..347e82df7 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -36,7 +36,7 @@ class DrawTriangleEnv(BaseEnv): """The brushes radius""" BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]] """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" - THRESHOLD = 0.05 + THRESHOLD = 0.02 SUPPORTED_REWARD_MODES = ["sparse"] @@ -212,8 +212,7 @@ def create_goal_triangle(name="tri", base_color=None): def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 with torch.device(self.device): - self.dot_pos = [] - b = self.num_envs + b = len(env_idx) self.table_scene.initialize(env_idx) target_pos = torch.zeros((b, 3)) @@ -222,21 +221,26 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) mats = quaternion_to_matrix(qs).to(self.device) self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) + + if hasattr(self, "vertices"): + self.vertices[env_idx] = torch.from_numpy( + np.tile(self.original_verts, (b, 1, 1)) + ).to( + self.device + ) # b, 3, 3 + else: + self.vertices = torch.from_numpy( + np.tile(self.original_verts, (b, 1, 1)) + ).to( + self.device + ) - self.vertices = torch.from_numpy( - np.tile(self.original_verts, (b, 1, 1)) - ).to( - self.device - ) # b, 3, 3 - print(self.original_verts) - print(self.vertices.shape) - - self.vertices = ( - mats.double() @ self.vertices.transpose(-1, -2).double() + self.vertices[env_idx] = ( + mats.double() @ self.vertices[env_idx].transpose(-1, -2).double() ).transpose( -1, -2 ) # apply rotation matrix - self.vertices += target_pos.unsqueeze(1) + self.vertices[env_idx] += target_pos.unsqueeze(1) self.triangles = self.generate_triangle_with_points( 100, self.vertices[:, :, :-1] @@ -271,7 +275,6 @@ def _after_control_step(self): self.DOT_THICKNESS / 2 + self.CANVAS_THICKNESS ) # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible - self.dot_pos.append(robot_brush_pos) new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) self.dots[self.draw_step].set_pose(new_dot_pos) @@ -319,11 +322,18 @@ def generate_triangle_with_points(self, n, vertices): return all_points def success_check(self): - - if len(self.dot_pos) == 0: + import time + a = time.time() + dot_pos = [] + for index in range(self.draw_step): + dot_pos.append(self.dots[index].pose.p) + print(time.time() -a) + + if len(dot_pos) < 10: return torch.zeros((self.num_envs), device=self.device).to(bool) - dot_pos = torch.vstack(self.dot_pos).reshape(self.num_envs, -1, 3) + a = time.time() + dot_pos = torch.vstack(dot_pos).reshape(self.num_envs, -1, 3) positive_z_mask = dot_pos[:, :, 2] > 0 batch_size = dot_pos.shape[0] From 1ffc230f2aaa6d96a053259a88e5e2aaf4f3e300 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 19 Nov 2024 09:30:15 +0000 Subject: [PATCH 32/39] success condition speed up --- mani_skill/envs/tasks/drawing/draw_svg.py | 87 +++++++++++++------ .../envs/tasks/drawing/draw_triangle.py | 86 +++++++++--------- 2 files changed, 106 insertions(+), 67 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 796174e16..55a0d9839 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -34,7 +34,7 @@ class DrawSVGEnv(BaseEnv): """The brushes radius""" BRUSH_COLORS = [[0.8, 0.2, 0.2, 1]] """The colors of the brushes. If there is more than one color, each parallel environment will have a randomly sampled color.""" - THRESHOLD = 0.05 + THRESHOLD = 0.1 SUPPORTED_REWARD_MODES = ["sparse"] @@ -98,8 +98,10 @@ def _load_scene(self, options: dict): import svgpathtools from svgpathtools import CubicBezier, Line, QuadraticBezier except ImportError: - raise ImportError("svgpathtools not installed. Install with pip install svgpathtools") - + raise ImportError( + "svgpathtools not installed. Install with pip install svgpathtools" + ) + self.table_scene = TableSceneBuilder(self, robot_init_qpos_noise=0) self.table_scene.build() @@ -207,7 +209,6 @@ def create_goal_outline(name="svg", base_color=None): self.canvas = self.canvas.build_static(name="canvas") self.dots = [] - self.dot_pos = None color_choices = torch.randint(0, len(self.BRUSH_COLORS), (self.num_envs,)) for i in range(self.MAX_DOTS): actors = [] @@ -241,6 +242,11 @@ def create_goal_outline(name="svg", base_color=None): base_color=np.array([10, 10, 10, 255]) / 255, ) + self.dots_dist = torch.ones((self.num_envs, 500)) * -1 + self.ref_dist = torch.zeros((self.num_envs, self.original_points.shape[0])).to( + bool + ) + def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 with torch.device(self.device): @@ -254,16 +260,27 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): rot_mat = quaternion_to_matrix(qs).to(self.device) self.goal_outline.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) - self.points = torch.from_numpy(np.tile(self.original_points, (b, 1, 1))).to( - self.device - ) # b, n, 3 + if hasattr(self, "vertices"): + self.points[env_idx] = torch.from_numpy( + np.tile(self.original_points, (b, 1, 1)) + ).to( + self.device + ) # b, 3, 3 + else: + self.points = torch.from_numpy( + np.tile(self.original_points, (b, 1, 1)) + ).to(self.device) - self.points = ( - rot_mat.double() @ self.points.transpose(-1, -2).double() + self.points[env_idx] = ( + rot_mat.double() @ self.points[env_idx].transpose(-1, -2).double() ).transpose( -1, -2 ) # rotation matrix - self.points += target_pos.unsqueeze(1) + self.points[env_idx] += target_pos.unsqueeze(1) + self.dots_dist[env_idx] = torch.ones((self.num_envs, 500)) * -1 + self.ref_dist[env_idx] = torch.zeros( + (self.num_envs, self.original_points.shape[0]) + ).to(bool) for dot in self.dots: # initially spawn dots in the table so they aren't seen @@ -296,12 +313,6 @@ def _after_control_step(self): # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) self.dots[self.draw_step].set_pose(new_dot_pos) - if new_dot_pos.get_p()[:, -1] > 0: - if self.dot_pos == None: - self.dot_pos = new_dot_pos.get_p()[:, None, :] - self.dot_pos = torch.cat( - (self.dot_pos, new_dot_pos.get_p()[:, None, :]), dim=1 - ) self.draw_step += 1 @@ -321,24 +332,44 @@ def _get_obs_extra(self, info: Dict): if "state" in self.obs_mode: obs.update( goal_pose=self.goal_outline.pose.raw_pose.reshape(self.num_envs, -1), - tcp_to_verts_pos=(self.points - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), + tcp_to_verts_pos=( + self.points - self.agent.tcp.pose.p.unsqueeze(1) + ).reshape(self.num_envs, -1), goal_pos=(self.goal_outline.pose.p).reshape(self.num_envs, -1), vertices=self.points.reshape(self.num_envs, -1), - continuous=torch.ones((self.num_envs, 1),device=self.device) * self.continuous # if the path is continuous + continuous=torch.ones((self.num_envs, 1), device=self.device) + * self.continuous, # if the path is continuous ) return obs def success_check(self): - if self.dot_pos == None or len(self.dot_pos) == 0: - return torch.Tensor([False]).to(bool) - drawn_pts = self.dot_pos[:, :, :-1] - - distance_matrix = torch.sqrt( - torch.sum( - (drawn_pts[:, :, None, :] - self.points[:, None, :, :2]) ** 2, axis=-1 + if self.draw_step > 0: + current_dot = self.dots[self.draw_step - 1].pose.p.reshape( + self.num_envs, 1, 3 + ) # b,3 + z_mask = current_dot[:, :, 2] < 0 + dist = ( + torch.sqrt( + torch.sum( + (current_dot[:, :, None, :2] - self.points[:, None, :, :2]) + ** 2, + dim=-1, + ) + ) + < self.THRESHOLD + ) + self.ref_dist = torch.logical_or( + self.ref_dist, (1 - z_mask.int()) * dist.reshape((self.num_envs, -1)) + ) + self.dots_dist[:, self.draw_step - 1] = torch.where( + z_mask, -1, torch.any(dist, dim=-1).reshape(self.num_envs, 1) ) - ) - Y_closeness = torch.min(distance_matrix, dim=1).values < self.THRESHOLD - return torch.Tensor([torch.all(Y_closeness)]).to(bool) + mask = self.dots_dist > -1 + # for valid drawn points + return torch.logical_and( + torch.all(self.dots_dist[mask], dim=-1), + torch.all(self.ref_dist, dim=-1), + ) + return torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 347e82df7..3ff76bc6d 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -1,5 +1,4 @@ import math -import random from typing import Dict import mani_skill.envs.utils.randomization as randomization @@ -17,8 +16,7 @@ from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig -from transforms3d.euler import euler2quat, quat2euler -from transforms3d.quaternions import quat2mat +from transforms3d.euler import euler2quat @register_env("DrawTriangle-v1", max_episode_steps=300) @@ -208,6 +206,8 @@ def create_goal_triangle(name="tri", base_color=None): name="goal_tri", base_color=np.array([10, 10, 10, 255]) / 255, ) + self.dots_dist = torch.ones((self.num_envs, 300)) * -1 + self.ref_dist = torch.zeros((self.num_envs, 153)).to(bool) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 @@ -221,7 +221,7 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): qs = randomization.random_quaternions(b, lock_x=True, lock_y=True) mats = quaternion_to_matrix(qs).to(self.device) self.goal_tri.set_pose(Pose.create_from_pq(p=target_pos, q=qs)) - + if hasattr(self, "vertices"): self.vertices[env_idx] = torch.from_numpy( np.tile(self.original_verts, (b, 1, 1)) @@ -231,9 +231,7 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): else: self.vertices = torch.from_numpy( np.tile(self.original_verts, (b, 1, 1)) - ).to( - self.device - ) + ).to(self.device) self.vertices[env_idx] = ( mats.double() @ self.vertices[env_idx].transpose(-1, -2).double() @@ -243,9 +241,12 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.vertices[env_idx] += target_pos.unsqueeze(1) self.triangles = self.generate_triangle_with_points( - 100, self.vertices[:, :, :-1] + self.n, self.vertices[:, :, :-1] ) + self.dots_dist[env_idx] = torch.ones((b, 300)) * -1 + self.ref_dist[env_idx] = torch.zeros((b, (self.n * 3) + 3)).to(bool) + for dot in self.dots: # initially spawn dots in the table so they aren't seen dot.set_pose( @@ -277,7 +278,7 @@ def _after_control_step(self): # move the next unused dot to the robot's brush position. All unused dots are initialized inside the table so they aren't visible new_dot_pos = Pose.create_from_pq(robot_brush_pos, euler2quat(0, np.pi / 2, 0)) self.dots[self.draw_step].set_pose(new_dot_pos) - + self.draw_step += 1 # on GPU sim we have to call _gpu_apply_all() to apply the changes we make to object poses. @@ -296,7 +297,9 @@ def _get_obs_extra(self, info: Dict): if "state" in self.obs_mode: obs.update( goal_pose=self.goal_tri.pose.raw_pose.reshape(self.num_envs, -1), - tcp_to_verts_pos=(self.vertices - self.agent.tcp.pose.p.unsqueeze(1)).reshape(self.num_envs, -1), + tcp_to_verts_pos=( + self.vertices - self.agent.tcp.pose.p.unsqueeze(1) + ).reshape(self.num_envs, -1), goal_pos=self.goal_tri.pose.p.reshape(self.num_envs, -1), vertices=self.vertices.reshape(self.num_envs, -1), ) @@ -304,6 +307,7 @@ def _get_obs_extra(self, info: Dict): return obs def generate_triangle_with_points(self, n, vertices): + # interpolates a triangle from vertices to have n points. total batch_size = vertices.shape[0] all_points = [] @@ -322,35 +326,39 @@ def generate_triangle_with_points(self, n, vertices): return all_points def success_check(self): - import time - a = time.time() - dot_pos = [] - for index in range(self.draw_step): - dot_pos.append(self.dots[index].pose.p) - print(time.time() -a) - - if len(dot_pos) < 10: - return torch.zeros((self.num_envs), device=self.device).to(bool) - - a = time.time() - dot_pos = torch.vstack(dot_pos).reshape(self.num_envs, -1, 3) - positive_z_mask = dot_pos[:, :, 2] > 0 - - batch_size = dot_pos.shape[0] - result = torch.zeros(batch_size, dtype=torch.bool, device=self.device) - - for b in range(batch_size): - valid_points = dot_pos[b][positive_z_mask[b]][...,:2] # shape: [num_valid_in_batch, 3] - if len(valid_points) > 0: - distance_matrix = torch.sqrt( + + if self.draw_step > 0: + current_dot = self.dots[self.draw_step - 1].pose.p.reshape( + self.num_envs, 1, 3 + ) # b,3 + z_mask = current_dot[:, :, 2] < 0 + + # distance for newly added pointed to all ref points + dist = ( + torch.sqrt( torch.sum( - (valid_points[:, None, :] - self.triangles[b]) ** 2, - dim=-1 + (current_dot[:, :, None, :2] - self.triangles[:, None, :, :2]) + ** 2, + dim=-1, ) - ) - min_distances = torch.min(distance_matrix, dim=1).values # shape: [num_valid_in_batch] - result[b] = torch.all(min_distances < self.THRESHOLD) - else: - result[b] = False + ) + < self.THRESHOLD + ) + + # if a reference point has a draw point near it + self.ref_dist = torch.logical_or( + self.ref_dist, (1 - z_mask.int()) * dist.reshape((self.num_envs, 153)) + ) + + # if current drawn point is close to a reference point. -1 if the drawn point hasn't actually been drawn yet + self.dots_dist[:, self.draw_step - 1] = torch.where( + z_mask, -1, torch.any(dist, dim=-1).reshape(self.num_envs, 1) + ) - return result + mask = self.dots_dist > -1 + # for valid drawn points + return torch.logical_and( + torch.all(self.dots_dist[mask], dim=-1), + torch.all(self.ref_dist, dim=-1), + ) + return torch.zeros(self.num_envs, dtype=torch.bool, device=self.device) From e8aa8d2fe44550057a93233007c0a3bcb0369b78 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 19 Nov 2024 09:35:02 +0000 Subject: [PATCH 33/39] small fix --- mani_skill/envs/tasks/drawing/draw_triangle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index 3ff76bc6d..c27088720 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -241,7 +241,7 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.vertices[env_idx] += target_pos.unsqueeze(1) self.triangles = self.generate_triangle_with_points( - self.n, self.vertices[:, :, :-1] + 50, self.vertices[:, :, :-1] ) self.dots_dist[env_idx] = torch.ones((b, 300)) * -1 From 3bfa2989ea5e8b7a5697244197c1e77c15c6247d Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 19 Nov 2024 09:38:36 +0000 Subject: [PATCH 34/39] Updated draw_triangle --- mani_skill/envs/tasks/drawing/draw_triangle.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index c27088720..ac04780cb 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -245,7 +245,7 @@ def _initialize_episode(self, env_idx: torch.Tensor, options: dict): ) self.dots_dist[env_idx] = torch.ones((b, 300)) * -1 - self.ref_dist[env_idx] = torch.zeros((b, (self.n * 3) + 3)).to(bool) + self.ref_dist[env_idx] = torch.zeros((b, 153)).to(bool) for dot in self.dots: # initially spawn dots in the table so they aren't seen From 6aa1793af80fabfc1140403069bcee3753da191c Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Tue, 19 Nov 2024 10:04:44 +0000 Subject: [PATCH 35/39] drawing env gpu bugfixes --- mani_skill/envs/tasks/drawing/draw_svg.py | 17 +++++++++-------- mani_skill/envs/tasks/drawing/draw_triangle.py | 11 ++++++----- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/mani_skill/envs/tasks/drawing/draw_svg.py b/mani_skill/envs/tasks/drawing/draw_svg.py index 55a0d9839..f338673a0 100644 --- a/mani_skill/envs/tasks/drawing/draw_svg.py +++ b/mani_skill/envs/tasks/drawing/draw_svg.py @@ -10,8 +10,7 @@ from mani_skill.utils import sapien_utils from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix from mani_skill.utils.registration import register_env -from mani_skill.utils.scene_builder.table.scene_builder import \ - TableSceneBuilder +from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig @@ -44,7 +43,7 @@ class DrawSVGEnv(BaseEnv): def __init__(self, *args, svg=None, robot_uids="panda_stick", **kwargs): if svg == None: - self.svg = "M7.875 0L0 7.875V55.125L7.875 63H23.763L23.7235 62.9292L11.8418 51.2859L11.8418 35.6268L21.1302 26.915L23.9193 11.6649L40.9773 6.3631L46.8835 16.5929L33.2356 19.926L32.6417 29.1349L41.1407 33.618L50.8511 23.465L56.6781 33.5577L43.5576 45.6794L28.9369 40.4365L26.1844 42.4266L26.1844 45.6794L43.2157 63H55.125L63 55.125V7.875L55.125 0H7.875Z" + self.svg = """M7.875 0L0 7.875V55.125L7.875 63H23.763L23.7235 62.9292L11.8418 51.2859L11.8418 35.6268L21.1302 26.915L23.9193 11.6649L40.9773 6.3631L46.8835 16.5929L33.2356 19.926L32.6417 29.1349L41.1407 33.618L50.8511 23.465L56.6781 33.5577L43.5576 45.6794L28.9369 40.4365L26.1844 42.4266L26.1844 45.6794L43.2157 63H55.125L63 55.125V7.875L55.125 0H7.875Z""" self.continuous = True else: self.svg = svg @@ -242,10 +241,10 @@ def create_goal_outline(name="svg", base_color=None): base_color=np.array([10, 10, 10, 255]) / 255, ) - self.dots_dist = torch.ones((self.num_envs, 500)) * -1 - self.ref_dist = torch.zeros((self.num_envs, self.original_points.shape[0])).to( - bool - ) + self.dots_dist = torch.ones((self.num_envs, 500), device=self.device) * -1 + self.ref_dist = torch.zeros( + (self.num_envs, self.original_points.shape[0]), device=self.device + ).to(bool) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 @@ -363,7 +362,9 @@ def success_check(self): self.ref_dist, (1 - z_mask.int()) * dist.reshape((self.num_envs, -1)) ) self.dots_dist[:, self.draw_step - 1] = torch.where( - z_mask, -1, torch.any(dist, dim=-1).reshape(self.num_envs, 1) + z_mask, -1, torch.any(dist, dim=-1) + ).reshape( + self.num_envs, ) mask = self.dots_dist > -1 diff --git a/mani_skill/envs/tasks/drawing/draw_triangle.py b/mani_skill/envs/tasks/drawing/draw_triangle.py index ac04780cb..4d5a23b58 100644 --- a/mani_skill/envs/tasks/drawing/draw_triangle.py +++ b/mani_skill/envs/tasks/drawing/draw_triangle.py @@ -11,8 +11,7 @@ from mani_skill.utils import sapien_utils from mani_skill.utils.geometry.rotation_conversions import quaternion_to_matrix from mani_skill.utils.registration import register_env -from mani_skill.utils.scene_builder.table.scene_builder import \ - TableSceneBuilder +from mani_skill.utils.scene_builder.table.scene_builder import TableSceneBuilder from mani_skill.utils.structs.actor import Actor from mani_skill.utils.structs.pose import Pose from mani_skill.utils.structs.types import SceneConfig, SimConfig @@ -206,8 +205,8 @@ def create_goal_triangle(name="tri", base_color=None): name="goal_tri", base_color=np.array([10, 10, 10, 255]) / 255, ) - self.dots_dist = torch.ones((self.num_envs, 300)) * -1 - self.ref_dist = torch.zeros((self.num_envs, 153)).to(bool) + self.dots_dist = torch.ones((self.num_envs, 300), device=self.device) * -1 + self.ref_dist = torch.zeros((self.num_envs, 153), device=self.device).to(bool) def _initialize_episode(self, env_idx: torch.Tensor, options: dict): self.draw_step = 0 @@ -352,7 +351,9 @@ def success_check(self): # if current drawn point is close to a reference point. -1 if the drawn point hasn't actually been drawn yet self.dots_dist[:, self.draw_step - 1] = torch.where( - z_mask, -1, torch.any(dist, dim=-1).reshape(self.num_envs, 1) + z_mask, -1, torch.any(dist, dim=-1) + ).reshape( + self.num_envs, ) mask = self.dots_dist > -1 From e4124aa388eebc99c671fa6b8bb4ea9ea8105350 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 21 Nov 2024 10:31:23 +0000 Subject: [PATCH 36/39] diff poll rgbd fixes --- .../diffusion_policy/utils.py | 4 +- .../baselines/diffusion_policy/train_rgbd.py | 52 ++++++++++--------- 2 files changed, 30 insertions(+), 26 deletions(-) diff --git a/examples/baselines/diffusion_policy/diffusion_policy/utils.py b/examples/baselines/diffusion_policy/diffusion_policy/utils.py index e3398ccee..7c87fa802 100644 --- a/examples/baselines/diffusion_policy/diffusion_policy/utils.py +++ b/examples/baselines/diffusion_policy/diffusion_policy/utils.py @@ -199,11 +199,11 @@ def build_obs_space(env, depth_dtype, state_obs_extractor): def build_state_obs_extractor(env_id): + agent_extra_envs = ["PickCube", "PushChair", "DrawTriangle", "DrawSVG"] env_name = env_id.split("-")[0] if env_name in ["TurnFaucet", "StackCube"]: return lambda obs: list(obs["extra"].values()) - elif env_name == "PushChair" or env_name == "PickCube": + elif env_name in agent_extra_envs: return lambda obs: list(obs["agent"].values()) + list(obs["extra"].values()) else: raise NotImplementedError(f"Please tune state obs by yourself") - diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index ef6e317f6..10c48a8e8 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -124,7 +124,7 @@ def reorder_keys(d, ref_dict): class SmallDemoDataset_DiffusionPolicy(Dataset): # Load everything into memory - def __init__(self, data_path, obs_process_fn, obs_space, num_traj): + def __init__(self, data_path, obs_process_fn, obs_space, device, num_traj): if data_path[-4:] == ".pkl": raise NotImplementedError() else: @@ -145,17 +145,21 @@ def __init__(self, data_path, obs_process_fn, obs_space, num_traj): _obs_traj_dict = obs_process_fn(_obs_traj_dict) _obs_traj_dict["depth"] = torch.Tensor( _obs_traj_dict["depth"].astype(np.float32) / 1024 - ).to(torch.float16) - _obs_traj_dict["rgb"] = torch.from_numpy( - _obs_traj_dict["rgb"] + ).to(device=device, dtype=torch.float16) + _obs_traj_dict["rgb"] = torch.from_numpy(_obs_traj_dict["rgb"]).to( + device ) # still uint8 - _obs_traj_dict["state"] = torch.from_numpy(_obs_traj_dict["state"]) + _obs_traj_dict["state"] = torch.from_numpy(_obs_traj_dict["state"]).to( + device + ) obs_traj_dict_list.append(_obs_traj_dict) trajectories["observations"] = obs_traj_dict_list self.obs_keys = list(_obs_traj_dict.keys()) # Pre-process the actions for i in range(len(trajectories["actions"])): - trajectories["actions"][i] = torch.Tensor(trajectories["actions"][i]) + trajectories["actions"][i] = torch.Tensor(trajectories["actions"][i]).to( + device=device + ) print( "Obs/action pre-processing is done, start to pre-compute the slice indices..." ) @@ -166,7 +170,7 @@ def __init__(self, data_path, obs_process_fn, obs_space, num_traj): or args.control_mode == "base_pd_joint_vel_arm_pd_joint_vel" ): self.pad_action_arm = torch.zeros( - (trajectories["actions"][0].shape[1] - 1,) + (trajectories["actions"][0].shape[1] - 1,), device=device ) # to make the arm stay still, we pad the action with 0 in 'delta_pos' control mode # gripper action needs to be copied from the last action @@ -368,6 +372,18 @@ def get_action(self, obs_seq): return noisy_action_seq[:, start:end] # (B, act_horizon, act_dim) +def save_ckpt(run_name, tag): + os.makedirs(f"runs/{run_name}/checkpoints", exist_ok=True) + ema.copy_to(ema_agent.parameters()) + torch.save( + { + "agent": agent.state_dict(), + "ema_agent": ema_agent.state_dict(), + }, + f"runs/{run_name}/checkpoints/{tag}.pt", + ) + + if __name__ == "__main__": args = tyro.cli(Args) @@ -459,18 +475,7 @@ def get_action(self, obs_seq): orignal_obs_space = tmp_env.observation_space tmp_env.close() dataset = SmallDemoDataset_DiffusionPolicy( - args.demo_path, obs_process_fn, orignal_obs_space, args.num_demos - ) - sampler = RandomSampler(dataset, replacement=False) - batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True) - batch_sampler = IterationBasedBatchSampler(batch_sampler, args.total_iters) - train_dataloader = DataLoader( - dataset, - batch_sampler=batch_sampler, - num_workers=args.num_dataload_workers, - worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed), - pin_memory=True, - persistent_workers=(args.num_dataload_workers > 0), + args.demo_path, obs_process_fn, orignal_obs_space, device, args.num_demos ) sampler = RandomSampler(dataset, replacement=False) batch_sampler = BatchSampler(sampler, batch_size=args.batch_size, drop_last=True) @@ -480,7 +485,6 @@ def get_action(self, obs_seq): batch_sampler=batch_sampler, num_workers=args.num_dataload_workers, worker_init_fn=lambda worker_id: worker_init_fn(worker_id, base_seed=args.seed), - pin_memory=True, persistent_workers=(args.num_dataload_workers > 0), ) @@ -518,10 +522,10 @@ def get_action(self, obs_seq): # forward and compute loss obs_batch_dict = data_batch["observations"] - obs_batch_dict = { - k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items() - } - act_batch = data_batch["actions"].cuda(non_blocking=True) + # obs_batch_dict = { + # k: v.cuda(non_blocking=True) for k, v in obs_batch_dict.items() + # } + act_batch = data_batch["actions"] # forward and compute loss total_loss = agent.compute_loss( From fa11b62fadcf5db97b444e1896f9ffe2be05ef27 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 19 Dec 2024 07:18:01 +0000 Subject: [PATCH 37/39] minor changes --- examples/baselines/diffusion_policy/train_rgbd.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index 10c48a8e8..017658dcd 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -144,7 +144,7 @@ def __init__(self, data_path, obs_process_fn, obs_space, device, num_traj): ) # key order in demo is different from key order in env obs _obs_traj_dict = obs_process_fn(_obs_traj_dict) _obs_traj_dict["depth"] = torch.Tensor( - _obs_traj_dict["depth"].astype(np.float32) / 1024 + _obs_traj_dict["depth"].astype(np.float32) ).to(device=device, dtype=torch.float16) _obs_traj_dict["rgb"] = torch.from_numpy(_obs_traj_dict["rgb"]).to( device @@ -285,7 +285,7 @@ def __init__(self, env, args): def encode_obs(self, obs_seq, eval_mode): rgb = obs_seq["rgb"].float() / 255.0 # (B, obs_horizon, 3*k, H, W) if args.depth: - depth = obs_seq["depth"].float() # (B, obs_horizon, 1*k, H, W) + depth = obs_seq["depth"].float() / 1024.0 # (B, obs_horizon, 1*k, H, W) img_seq = torch.cat([rgb, depth], dim=2) # (B, obs_horizon, C, H, W), C=4*k else: img_seq = rgb @@ -340,7 +340,7 @@ def get_action(self, obs_seq): with torch.no_grad(): obs_seq["rgb"] = obs_seq["rgb"].permute(0, 1, 4, 2, 3) if args.depth: - obs_seq["depth"] = obs_seq["depth"].permute(0, 1, 4, 2, 3) / 1024 + obs_seq["depth"] = obs_seq["depth"].permute(0, 1, 4, 2, 3) obs_cond = self.encode_obs( obs_seq, eval_mode=True From 68dee3cfdd0d1a015658bd4c15cd7633baf7fe62 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Thu, 19 Dec 2024 20:15:39 +0000 Subject: [PATCH 38/39] fix for wandb logging --- examples/baselines/diffusion_policy/train_rgbd.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/examples/baselines/diffusion_policy/train_rgbd.py b/examples/baselines/diffusion_policy/train_rgbd.py index 017658dcd..83123fccc 100644 --- a/examples/baselines/diffusion_policy/train_rgbd.py +++ b/examples/baselines/diffusion_policy/train_rgbd.py @@ -440,11 +440,19 @@ def save_ckpt(run_name, tag): import wandb config = vars(args) + + # TODO: Check if this is needed (if so non rgbd could use this as well) + # deals with gym utils not being able to access max episode steps for async vector envs + if args.sim_backend == "cpu" and args.num_eval_envs > 1: + assert args.max_episode_steps != None, "If using cpu environments max_steps must be specified" + horizon = args.max_episode_steps + else: + horizon = gym_utils.find_max_episode_steps_value(envs) config["eval_env_cfg"] = dict( **env_kwargs, num_envs=args.num_eval_envs, env_id=args.env_id, - env_horizon=gym_utils.find_max_episode_steps_value(envs), + env_horizon=horizon, ) wandb.init( project=args.wandb_project_name, From 76447652f1dfda64db3f4515085652e07ceaf751 Mon Sep 17 00:00:00 2001 From: Arnav G <73305591+arnavg115@users.noreply.github.com> Date: Mon, 30 Dec 2024 18:22:06 +0000 Subject: [PATCH 39/39] autobuild docs for DrawTriangle and SVG --- .../DrawSVG-v1_rt_thumb_first.png | Bin 0 -> 43527 bytes .../DrawSVG-v1_rt_thumb_last.png | Bin 0 -> 44620 bytes .../DrawTriangle-v1_rt_thumb_first.png | Bin 0 -> 42529 bytes .../DrawTriangle-v1_rt_thumb_last.png | Bin 0 -> 42388 bytes docs/source/tasks/drawing/index.md | 58 ++++++++++++------ mani_skill/envs/tasks/drawing/draw_svg.py | 14 +++++ .../envs/tasks/drawing/draw_triangle.py | 15 +++++ 7 files changed, 68 insertions(+), 19 deletions(-) create mode 100644 docs/source/_static/env_thumbnails/DrawSVG-v1_rt_thumb_first.png create mode 100644 docs/source/_static/env_thumbnails/DrawSVG-v1_rt_thumb_last.png create mode 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    1000

    + +

    DrawSVG-v1

    +
    DrawSVG-v1 DrawSVG-v1
    +

    +

    +

    +

    500

    + + +

    DrawTriangle-v1

    +
    DrawTriangle-v1 DrawTriangle-v1
    +

    +

    +

    +

    300

    + @@ -63,48 +79,52 @@ None -## DrawTriangle-v1 +## DrawSVG-v1 +![no-dense-reward][no-dense-reward-badge] +![sparse-reward][sparse-reward-badge] :::{dropdown} Task Card :icon: note :color: primary **Task Description:** -Instantiates a table with a white canvas on it and a goal triangle with an outline. A robot with a stick is to draw the triangle with a red line. - -**Supported Robots: PandaStick** +Instantiates a table with a white canvas on it and a svg path specified with an outline. A robot with a stick is to draw the triangle with a red line. **Randomizations:** -- the goal triangle's position on the xy-plane is randomized -- the goal triangle's z-rotation is randomized in range [0, 2 $\pi$] +- the goal svg's position on the xy-plane is randomized +- the goal svg's z-rotation is randomized in range [0, 2 $\pi$] **Success Conditions:** -- the drawn points by the robot are within a euclidean distance of 0.05m with points on the goal triangle +- the drawn points by the robot are within a euclidean distance of 0.05m with points on the goal svg ::: -

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