examples/sipp_planner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/rainorangelemon/Documents/lemp\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pybullet build time: May 20 2022 19:44:17\n",
      "/home/rainorangelemon/anaconda3/envs/gnnmp/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from environment.dynamic.simple2arm_env import Simple2ArmEnv\n",
    "from objects.dynamic_object import DynamicObject\n",
    "from robot.abstract_robot import DynamicRobotFactory\n",
    "from objects.trajectory import WaypointLinearTrajectory\n",
    "from robot.simple2arm_robot import Simple2ArmRobot\n",
    "from planner.sipp_planner import SippPlanner\n",
    "\n",
    "import numpy as np\n",
    "# visualize environment\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from utils.utils import seed_everything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "waypoints = np.linspace(0, np.pi/4, 20).tolist()\n",
    "waypoints = [np.array([p,p]) for p in waypoints]\n",
    "traj = WaypointLinearTrajectory(waypoints=waypoints)\n",
    "robot = Simple2ArmRobot(base_position=(1, 1, 0), base_orientation=(0, 0, 0, 1))\n",
    "objs = DynamicObject(item=robot, trajectory=traj)\n",
    "\n",
    "env = Simple2ArmEnv(objects=[objs])\n",
    "num_samples = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading dynamic object\n",
      "Loading robot from ../data/robot/simple2arm/2dof.urdf\n",
      "Robot loaded with item_id 0\n",
      "Loading robot from ../data/robot/simple2arm/2dof.urdf\n",
      "Robot loaded with item_id 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.load()\n",
    "plt.imshow(env.render())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success\n"
     ]
    }
   ],
   "source": [
    "start, goal = np.array([0.] * 2), np.array([np.pi*7/8, 0])\n",
    "while True:\n",
    "    seed_everything(42)\n",
    "    initial_planner = SippPlanner(num_samples=num_samples, stop_when_success=True)\n",
    "    result = initial_planner.plan(env, start, goal, timeout=('time', 1000))\n",
    "    if result.solution:\n",
    "        print('success')\n",
    "        break\n",
    "    else:\n",
    "        # sample a non-trival problem setting and solve\n",
    "        start, goal = env.robot.sample_random_init_goal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solution': [array([0., 0.]),\n",
       "  array([0.00647693, 0.05223153]),\n",
       "  array([0.01295385, 0.10446306]),\n",
       "  array([0.01943078, 0.15669458]),\n",
       "  array([0.0259077 , 0.20892611]),\n",
       "  array([0.03238463, 0.26115764]),\n",
       "  array([0.03886156, 0.31338917]),\n",
       "  array([0.04519555, 0.36446809]),\n",
       "  array([0.05120066, 0.41675597]),\n",
       "  array([0.05720577, 0.46904384]),\n",
       "  array([0.06321087, 0.52133171]),\n",
       "  array([0.06921598, 0.57361959]),\n",
       "  array([0.07522109, 0.62590746]),\n",
       "  array([0.08122619, 0.67819534]),\n",
       "  array([0.0872313 , 0.73048321]),\n",
       "  array([0.0932364 , 0.78277109]),\n",
       "  array([0.09791504, 0.8235091 ]),\n",
       "  array([0.10935528, 0.87488228]),\n",
       "  array([0.12079551, 0.92625547]),\n",
       "  array([0.13223574, 0.97762866]),\n",
       "  array([0.14201369, 1.02153724]),\n",
       "  array([0.13890662, 1.07407703]),\n",
       "  array([0.13579955, 1.12661681]),\n",
       "  array([0.13269249, 1.1791566 ]),\n",
       "  array([0.12958542, 1.23169639]),\n",
       "  array([0.12647836, 1.28423617]),\n",
       "  array([0.12337129, 1.33677596]),\n",
       "  array([0.12026422, 1.38931575]),\n",
       "  array([0.11715716, 1.44185554]),\n",
       "  array([0.11476596, 1.48229021]),\n",
       "  array([0.12620973, 1.5336626 ]),\n",
       "  array([0.1376535, 1.585035 ]),\n",
       "  array([0.14909727, 1.6364074 ]),\n",
       "  array([0.16054105, 1.6877798 ]),\n",
       "  array([0.16318983, 1.69967049]),\n",
       "  array([0.20880888, 1.72591903]),\n",
       "  array([0.23172121, 1.73910245]),\n",
       "  array([0.27698211, 1.71224107]),\n",
       "  array([0.32224301, 1.68537968]),\n",
       "  array([0.36750391, 1.6585183 ]),\n",
       "  array([0.39897001, 1.63984384]),\n",
       "  array([0.44177125, 1.60921481]),\n",
       "  array([0.48457249, 1.57858579]),\n",
       "  array([0.52737373, 1.54795677]),\n",
       "  array([0.57017497, 1.51732775]),\n",
       "  array([0.61297621, 1.48669873]),\n",
       "  array([0.65577745, 1.45606971]),\n",
       "  array([0.69857869, 1.42544069]),\n",
       "  array([0.70525914, 1.42066008]),\n",
       "  array([0.74516475, 1.38634353]),\n",
       "  array([0.78507036, 1.35202698]),\n",
       "  array([0.82497596, 1.31771042]),\n",
       "  array([0.86488157, 1.28339387]),\n",
       "  array([0.90478717, 1.24907732]),\n",
       "  array([0.94469278, 1.21476077]),\n",
       "  array([0.98459838, 1.18044422]),\n",
       "  array([1.01919951, 1.15068922]),\n",
       "  array([1.0623017 , 1.12048518]),\n",
       "  array([1.10540389, 1.09028114]),\n",
       "  array([1.14850609, 1.0600771 ]),\n",
       "  array([1.19160828, 1.02987306]),\n",
       "  array([1.23471047, 0.99966902]),\n",
       "  array([1.27781267, 0.96946498]),\n",
       "  array([1.32091486, 0.93926094]),\n",
       "  array([1.35630736, 0.9144595 ]),\n",
       "  array([1.40066003, 0.88612359]),\n",
       "  array([1.44501269, 0.85778767]),\n",
       "  array([1.48936536, 0.82945176]),\n",
       "  array([1.52563732, 0.80627842]),\n",
       "  array([1.57217197, 0.7816896 ]),\n",
       "  array([1.61870661, 0.75710078]),\n",
       "  array([1.66524125, 0.73251196]),\n",
       "  array([1.7117759 , 0.70792314]),\n",
       "  array([1.75831054, 0.68333433]),\n",
       "  array([1.80484519, 0.65874551]),\n",
       "  array([1.85137983, 0.63415669]),\n",
       "  array([1.89791447, 0.60956787]),\n",
       "  array([1.93419431, 0.59039767]),\n",
       "  array([1.98160559, 0.5675448 ]),\n",
       "  array([2.02901687, 0.54469193]),\n",
       "  array([2.07642815, 0.52183906]),\n",
       "  array([2.12383944, 0.49898619]),\n",
       "  array([2.17125072, 0.47613332]),\n",
       "  array([2.218662  , 0.45328046]),\n",
       "  array([2.26607328, 0.43042759]),\n",
       "  array([2.30197489, 0.41312253]),\n",
       "  array([2.34062365, 0.3773964 ]),\n",
       "  array([2.37927241, 0.34167027]),\n",
       "  array([2.41792118, 0.30594414]),\n",
       "  array([2.45656994, 0.27021802]),\n",
       "  array([2.4952187 , 0.23449189]),\n",
       "  array([2.53386746, 0.19876576]),\n",
       "  array([2.57251623, 0.16303963]),\n",
       "  array([2.61116499, 0.1273135 ]),\n",
       "  array([2.64981375, 0.09158737]),\n",
       "  array([2.68846251, 0.05586124]),\n",
       "  array([2.72711127, 0.02013511]),\n",
       "  array([2.74889357, 0.        ])],\n",
       " 'running_time': 27.361577853560448,\n",
       " 'num_collision_check': 1750384,\n",
       " 'num_node': None}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Visualization\n",
    "from time import sleep\n",
    "def visualize_traj(env, traj_agent):\n",
    "    max_len_traj = max([len(obj.trajectory.waypoints) for obj in env.objects])\n",
    "                           \n",
    "    max_time_obs = max_len_traj-1\n",
    "    speed = 1 / max_time_obs\n",
    "    gifs = []\n",
    "    max_len = max(len(traj_agent.waypoints), max_len_traj)\n",
    "\n",
    "    for timestep in np.linspace(0,  max_len-1, 100):\n",
    "        env.robot.set_config(traj_agent.get_spec(timestep))\n",
    "        for obj in env.objects:\n",
    "            obj.set_config_at_time(timestep)\n",
    "        p.performCollisionDetection()\n",
    "        sleep(0.1)\n",
    "        gifs.append(p.getCameraImage(width=360, height=360, lightDirection=[1, 1, 1], shadow=1,\n",
    "                                                 renderer=p.ER_BULLET_HARDWARE_OPENGL)[2])\n",
    "    return gifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "argv[0]=--background_color_red=1.0\n",
      "argv[1]=--background_color_green=1.0\n",
      "argv[2]=--background_color_blue=1.0\n",
      "startThreads creating 1 threads.\n",
      "starting thread 0\n",
      "started thread 0 \n",
      "argc=5\n",
      "argv[0] = --unused\n",
      "argv[1] = --background_color_red=1.0\n",
      "argv[2] = --background_color_green=1.0\n",
      "argv[3] = --background_color_blue=1.0\n",
      "argv[4] = --start_demo_name=Physics Server\n",
      "ExampleBrowserThreadFunc started\n",
      "X11 functions dynamically loaded using dlopen/dlsym OK!\n",
      "X11 functions dynamically loaded using dlopen/dlsym OK!\n",
      "Creating context\n",
      "Created GL 3.3 context\n",
      "Direct GLX rendering context obtained\n",
      "Making context current\n",
      "GL_VENDOR=NVIDIA Corporation\n",
      "GL_RENDERER=NVIDIA GeForce RTX 3080/PCIe/SSE2\n",
      "GL_VERSION=3.3.0 NVIDIA 470.161.03\n",
      "GL_SHADING_LANGUAGE_VERSION=3.30 NVIDIA via Cg compiler\n",
      "pthread_getconcurrency()=0\n",
      "Version = 3.3.0 NVIDIA 470.161.03\n",
      "Vendor = NVIDIA Corporation\n",
      "Renderer = NVIDIA GeForce RTX 3080/PCIe/SSE2\n",
      "b3Printf: Selected demo: Physics Server\n",
      "startThreads creating 1 threads.\n",
      "starting thread 0\n",
      "started thread 0 \n",
      "MotionThreadFunc thread started\n",
      "ven = NVIDIA Corporation\n",
      "ven = NVIDIA Corporation\n",
      "loading dynamic object\n",
      "Loading robot from ../data/robot/simple2arm/2dof.urdf\n",
      "Robot loaded with item_id 0\n",
      "Loading robot from ../data/robot/simple2arm/2dof.urdf\n",
      "Robot loaded with item_id 1\n"
     ]
    },
    {
     "data": {
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/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pybullet as p\n",
    "import pybullet_data\n",
    "import numpy as np\n",
    "from utils.utils import save_gif\n",
    "from IPython.display import HTML\n",
    "import base64\n",
    "from objects.trajectory import WaypointLinearTrajectory\n",
    "\n",
    "env.load(GUI=True)\n",
    "\n",
    "traj_agent = WaypointLinearTrajectory(result.solution)\n",
    "gifs = visualize_traj(env, traj_agent)\n",
    "save_gif(gifs, f'data/visualization/sipp.gif')\n",
    "b64 = base64.b64encode(open(f'data/visualization/sipp.gif', 'rb').read()).decode('ascii')\n",
    "display(HTML(f'<img src=\"data:image/gif;base64,{b64}\" />'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "c1daed7e4d34e14fbb9bb260207f2e4074aea67593e999acdc484a19561ddc79"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}