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Learning a Decentralized Multiarm Motion Planner
Huy Ha, Jingxi Xu, Shuran Song, <br> Columbia University, New York, NY, United States<br> 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}
}