examples/dynamic_gnn_planner.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "> [GNN-Dynamic](https://arxiv.org/abs/2210.08408) - it plans a trajectory by prioritizing the edge to explore based on a Graph Neural Network heuristic function in a dynamic environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/ruipeng/Desktop/lemp\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pybullet build time: Mar 16 2022 13:26:26\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",
    "from utils.utils import seed_everything\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "waypoints = np.linspace(0, np.pi/4, 40).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\n",
    "max_num_samples = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize environment\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "env.load()\n",
    "plt.imshow(env.render())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from planner.learned.GNN_dynamic_planner import GNNDynamicPlanner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_path = ['data/weights/dynamic/2arms/weights_gnn.pt','data/weights/dynamic/2arms/weights_head.pt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruipeng/Desktop/lemp/planner/learned/model/base_models.py:77: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
      "  freqs = min_freq ** (2 * (torch.arange(self.embed_size) // 2) / self.embed_size)\n",
      "/Users/ruipeng/Desktop/lemp/planner/learned/GNN_dynamic_planner.py:93: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /Users/distiller/project/pytorch/torch/csrc/utils/tensor_new.cpp:210.)\n",
      "  data.v = torch.FloatTensor(points)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success {'solution': [array([0., 0.], dtype=float32), array([0.052411  , 0.00481347], dtype=float32), array([0.10482201, 0.00962694], dtype=float32), array([0.15723301, 0.01444041], dtype=float32), array([0.20964402, 0.01925389], dtype=float32), array([0.26205504, 0.02406736], dtype=float32), array([0.31446603, 0.02888083], dtype=float32), array([0.36687702, 0.0336943 ], dtype=float32), array([0.41151342, 0.03779374], dtype=float32), array([0.46414185, 0.03836958], dtype=float32), array([0.51677024, 0.03894541], dtype=float32), array([0.5693987 , 0.03952125], dtype=float32), array([0.62202716, 0.04009709], dtype=float32), array([0.67465556, 0.04067292], dtype=float32), array([0.72728395, 0.04124876], dtype=float32), array([0.7799124 , 0.04182459], dtype=float32), array([0.83254087, 0.04240043], dtype=float32), array([0.8340862 , 0.04241734], dtype=float32), array([0.8865944 , 0.04601858], dtype=float32), array([0.93910265, 0.04961981], dtype=float32), array([0.9916109 , 0.05322105], dtype=float32), array([1.0441191 , 0.05682229], dtype=float32), array([1.0966274 , 0.06042352], dtype=float32), array([1.1491356 , 0.06402476], dtype=float32), array([1.2016438 , 0.06762599], dtype=float32), array([1.254152  , 0.07122723], dtype=float32), array([1.3066603 , 0.07482847], dtype=float32), array([1.3395048 , 0.07708108], dtype=float32), array([1.3918142 , 0.08289561], dtype=float32), array([1.4441236 , 0.08871014], dtype=float32), array([1.496433  , 0.09452467], dtype=float32), array([1.5487425, 0.1003392], dtype=float32), array([1.6010519 , 0.10615373], dtype=float32), array([1.639276  , 0.11040258], dtype=float32), array([1.6908237 , 0.12102918], dtype=float32), array([1.7423713 , 0.13165578], dtype=float32), array([1.793919  , 0.14228237], dtype=float32), array([1.8454666 , 0.15290898], dtype=float32), array([1.8970141 , 0.16353557], dtype=float32), array([1.9485618 , 0.17416216], dtype=float32), array([2.0001094 , 0.18478876], dtype=float32), array([2.023407  , 0.18959157], dtype=float32), array([2.074234, 0.175928], dtype=float32), array([2.125061  , 0.16226444], dtype=float32), array([2.175888  , 0.14860086], dtype=float32), array([2.2267153 , 0.13493729], dtype=float32), array([2.2775424 , 0.12127371], dtype=float32), array([2.278301  , 0.12106975], dtype=float32), array([2.3292727 , 0.10795621], dtype=float32), array([2.3802445 , 0.09484266], dtype=float32), array([2.4312162 , 0.08172911], dtype=float32), array([2.482188  , 0.06861557], dtype=float32), array([2.5331597 , 0.05550203], dtype=float32), array([2.5841315 , 0.04238848], dtype=float32), array([2.6351032 , 0.02927494], dtype=float32), array([2.686075 , 0.0161614], dtype=float32), array([2.7370467 , 0.00304785], dtype=float32), array([2.7488935, 0.       ], dtype=float32)], 'running_time': 1.6064062770000005, 'num_collision_check': 1336, 'num_node': 1000}\n"
     ]
    }
   ],
   "source": [
    "start, goal = np.array([0.] * 2), np.array([np.pi*7/8, 0])\n",
    "# start, goal = np.array([0.] * 2), np.array([0, 0])\n",
    "seed_everything(42)\n",
    "model_args = dict(config_size=env.robot.config_dim, embed_size=32, obs_size=9)\n",
    "planner = GNNDynamicPlanner(num_samples=num_samples, max_num_samples=max_num_samples, model_args=model_args, use_bctc=False, stop_when_success=True)\n",
    "# load trained weights\n",
    "planner.load_model(weights_path)\n",
    "result = planner.plan(env, start, goal, timeout=('time', 1000))\n",
    "if result.solution:\n",
    "    print('success', result)\n",
    "else:\n",
    "    print('fail')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "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/GNN-Dynamic.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": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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"
     ]
    },
    {
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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/GNN-Dynamic.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}\" />'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (GNN-Dynamic)",
   "language": "python",
   "name": "pycharm-e8cae0ef"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}