Learning Obstacle Representations for Neural Motion Planning

Robin Strudel, Ricardo Garcia, Justin Carpentier, Jean-Paul Laumond, Ivan Laptev, Cordelia Schmid
CoRL 2020

• Project Page
• Paper

Table of Content

• Cite
• Setup
• Training
  • Train on narrow passages
  • Train on 3D environments
  • Monitor experiments
• Run
• Logging

Cite

Please cite our work if you use our code or compare to our approach

@inproceedings{strudelnmp2020,
title={Learning Obstacle Representations for Neural Motion Planning},
author={R. {Strudel} and R. {Garcia} and J. {Carpentier} and J.P. {Laumond} and I. {Laptev} and C. {Schmid}},
journal={Proceedings of Conference on Robot Learning (CoRL)},
year={2020}
}

Setup

Download the code

git clone https://github.com/rstrudel/nmprepr
cd nmprepr

To create a new conda environment containing dependencies

conda env create -f environment.yml
conda activate nmprepr

To update a conda environment with dependencies

conda env update -f environment.yml

Train

Narrow passages

To train a planning policy on 2D environments with narrow passages

python -m nmp.train Narrow-64Pts-LocalSurfaceNormals-v0 narrow --horizon 50 --seed 0

3D environments

To train a planning policy for the Sphere

python -m nmp.train Sphere-Boxes-64Pts-Rays-v0 sphere_boxes --horizon 80 --seed 0

To train planning policies for the S-Shape

python -m nmp.train SShape-Boxes-64Pts-Rays-v0 sshape_boxes --horizon 80 --seed 0

Monitor

You can monitor experiments with

tensorboard --logdir=/path/to/experiment

Run

Launch gepetto-gui in a separate terminal

gepetto-gui

Run a planning policy for the S-Shape and visualize it with gepetto-gui

python -m nmp.run SShape-Boxes-64Pts-Rays-v0 --exp-name log_dir/params.pkl --seed 100 --horizon 80

Evaluate the success rate of a policy on 100 episodes

python -m nmp.run SShape-Boxes-64Pts-Rays-v0 --exp-name log_dir/params.pkl --seed 100 --horizon 80 --episodes 100

Logging

By default the checkpointing will be in your home directory. You can change it by defining a CHECKPOINT environment variable. Add the following to your .bashrc file to change the logging directory.

export CHECKPOINT=/path/to/checkpoints