merge new examples

mani_skill2/examples/demo_scenes.py

@@ -0,0 +1,56 @@
+"""
+The code at the moment supports scenes created by [AI2-THOR](https://ai2thor.allenai.org/) stored in this 
+Hugging Face Dataset: https://huggingface.co/datasets/hssd/ai2thor-hab/tree/main
+
+To download the dataset, run `python -m mani_skill2.utils.scene_builder.ai2thor.download <HUGGING_FACE_TOKEN>` 
+and make sure to pass in your hugging face API token. Note that you must create a hugging face account 
+and accept the terms of use for the dataset. Alternatively, if you can download the data through other means, simply
+save it to `data/scene_datasets/ai2thor` and the code should run.
+
+To learn how scenes are imported and built in ManiSkill, check out mani_skill2/utils/scene_builder module, there are some prebuilt scenes, 
+including code that imports scenes in the AI2THOR set of scenes and format, as well as code to build simple table-top scenes commonly used in
+ManiSkill.
+
+PickObjectScene-v0 selects a scene randomly from the given SceneBuilder and
+instantiates a robot randomly and selects a random object for the robot to find and pick up.
+render_mode="human" opens up a viewer, convex_decomposition="none" makes scene loading fast (but not well simulated)
+set convex_decomposition="coacd" to use CoACD to get better collision meshes
+
+Code is setup so that if you press the "r" key, a new scene is loaded and shown. You can run this file by running 
+`python -m mani_skill2.examples.demo_scenes` and explore around.
+"""
+import gymnasium as gym
+import sapien.render
+import numpy as np
+import mani_skill2.envs
+from mani_skill2.utils.scene_builder.ai2thor import (
+    ArchitecTHORSceneBuilder,
+    ProcTHORSceneBuilder,
+    RoboTHORSceneBuilder,
+    iTHORSceneBuilder,
+)
+
+if __name__ == "__main__":
+    # specify we want to sample from the ArchitecTHOR set of scenes. Other SceneBuilders are imported above and can be used
+    env = gym.make(
+        "PickObjectScene-v0",
+        render_mode="human",
+        scene_builder_cls=ArchitecTHORSceneBuilder,
+        convex_decomposition="none",
+        fixed_scene=True,
+    )
+
+    # optionally set these to make it more realistic
+    sapien.render.set_camera_shader_dir("rt")
+    sapien.render.set_viewer_shader_dir("rt")
+    sapien.render.set_ray_tracing_samples_per_pixel(4)
+    sapien.render.set_ray_tracing_path_depth(2)
+    sapien.render.set_ray_tracing_denoiser("optix")
+
+    env.reset(seed=np.random.randint(2**31), options=dict(reconfigure=True))
+    viewer = env.render()
+    while True:
+        env.render()
+        if viewer.window.key_down("r"):
+            env.reset(options=dict(reconfigure=True))
+            viewer = env.render()

mani_skill2/examples/motionplanning/README.md

@@ -1,7 +1,6 @@
 # Motion Planning Examples
 
-To run the motion planner code to solve tasks / generate demonstrations, first install ManiSkill2 and then install mplib, a simple python motion planner library we maintain
-
+First install the maniskill2 motion planning library
 ```
 pip install mplib
 ```

mani_skill2/examples/motionplanning/utils.py

@@ -6,7 +6,7 @@
 from transforms3d import quaternions
 
 from mani_skill2.utils.common import normalize_vector
-from mani_skill2.utils.trimesh_utils import get_component_mesh
+from mani_skill2.utils.geometry.trimesh_utils import get_component_mesh
 
 
 def get_actor_obb(actor: sapien.Entity, to_world_frame=True, vis=False):