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GNN-dynamic models + GNN-dynamic-planner
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,195 @@ | ||
| { | ||
| "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": null, | ||
| "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" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# visualize environment\n", | ||
| "%matplotlib inline\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "env.load()\n", | ||
| "plt.imshow(env.render())\n", | ||
| "plt.show()" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from planner.learned.GNN_dynamic_planner import GNNDynamicPlanner" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [ | ||
| "weights_path = ['data/weights/dynamic/2arms/weights_gnn.pt','data/weights/dynamic/2arms/weights_head.pt']" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "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')" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "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 | ||
| } |
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