Robin Strudel, Ricardo Garcia, Justin Carpentier, Jean-Paul Laumond, Ivan Laptev, Cordelia Schmid
CoRL 2020
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}
}
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
To train a planning policy on 2D environments with narrow passages
python -m nmp.train Narrow-64Pts-LocalSurfaceNormals-v0 narrow --horizon 50 --seed 0
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
You can monitor experiments with
tensorboard --logdir=/path/to/experiment
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
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