DexDeform

Code and data for paper DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics at ICLR 2023.

Installation

conda env create -f environment.yml
conda activate dexdeform
pip install -e .

Install Sinkhorn Distance Metric

pip install pykeops
pip install geomloss

Download Demonstrations

Download here. For loading demonstrations, checkout tutorials/demonstration_loading.ipynb.

Tutorials

• [Environment Loading] tutorials/1_environment_loading.ipynb
• [Trajectory Optimization] tutorials/2_trajectory_optimization.ipynb
• [Leap motion tracking module] leap_motion/
• [Demonstration Loading] tutorials/3_demonstration_loading.ipynb
• [Computing Score] tutorials/4_computing_score.ipynb

Implementation Details

• Our simulation backend supports full differentiability and communications with PyTorch modules.
• For optimal performance, the simulation backend is written in CUDA and implements PlasticineLab.
• We provide python wrapper for the dexterous hand environment, located inside hand.py.

Acknowledgements

• Our physics simulation is written based on PlasticineLab.
• Our leap motion tracking module is written based on this repo.

TODO

• Support for Human Teleoperation (Leap motion tracking module released, synchronization with simulation coming soon)
• Release demonstrations
• Support for DexDeform Algorithm (template uploaded, cleanup needed to support dataloding.)

Citation

@inproceedings{
li2023dexdeform,
title={DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics},
author={Sizhe Li and Zhiao Huang and Tao Chen and Tao Du and Hao Su and Joshua B. Tenenbaum and Chuang Gan},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=LIV7-_7pYPl}
}