Learning a Decentralized Multiarm Motion Planner

Huy Ha, Jingxi Xu, Shuran Song,
Columbia University, New York, NY, United States
CoRL 2020

Project Page | Video | Arxiv

Visualizations created using PyBullet-Blender recorder

Setup

Python 3.7 dependencies:

• PyTorch 1.6.0
• pybullet
• numpy
• numpy-quaternion
• ray
• tensorboardX

We've prepared a conda YAML file which contains all the necessary dependencies. To use it, run

conda env create -f environment.yml
conda activate multiarm

Evaluate the pretrained motion planner

In the repo's root, download the pretrained weights and evaluation benchmark

wget -qO- https://multiarm.cs.columbia.edu/downloads/checkpoints/ours.tar.xz | tar xvfJ -
wget -qO- https://multiarm.cs.columbia.edu/downloads/data/benchmark.tar.xz | tar xvfJ -

Then evaluate the pretrained weights on the benchmark in static mode with

python main.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 1 --gui

You can remove --gui to run headless, and use more CPU cores with --num_processes 16.

To summarize the benchmark results

python summary.py ours/benchmark_score.pkl

To evaluate the pretrained weights on the benchmark in dynamic mode, run

python benchmark_dynamic.py --mode benchmark --tasks_path benchmark/ --load ours/ours.pth --num_processes 1 --gui

Train a decentralized multi-arm motion planner

In the repo's root, download the training tasks and expert demonstration dataset

wget -qO- https://multiarm.cs.columbia.edu/downloads/data/tasks.tar.xz | tar xvfJ -
wget -qO- https://multiarm.cs.columbia.edu/downloads/data/expert.tar.xz | tar xvfJ -

Then train a decentralized multi-arm motion planner from scratch with

mkdir runs
python main.py --config configs/default.json --tasks_path tasks/ --expert_waypoints expert/ --num_processes 16 --name multiarm_motion_planner

Running the 6 DOF Bin Pick and Place Demo

See demo/README.md for instructions.

Citation

@inproceedings{ha2020multiarm,
	title={Learning a Decentralized Multi-arm Motion Planner},
	author={Ha, Huy and Xu, Jingxi and Song, Shuran},
	booktitle={Conference on Robotic Learning (CoRL)},
	year={2020}
}