chopsticks-research2022 / learning2usechopsticks

Learning to Use Chopsticks in Diverse Gripping Styles

Zeshi Yang, KangKang Yin, and Libin Liu

ACM Trans. Graph., Vol. 41, No. 4, Article 95. (SIGGRAPH 2022)

Paper Video

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Table of Contents

1. Citation
2. Requirements and Dependencies
3. Installation
4. Running demo
5. Training

Citation

@article{yang2022chop
author = {Yang, Zeshi and Yin, Kangkang and Liu, Libin},
title = {Learning to Use Chopsticks in Diverse Gripping Styles},
year = {2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {41},
number = {4},
journal = {ACM Trans. Graph.},
articleno = {95},
numpages = {17}
}

Requirements and Dependencies

• mujoco_py (version:1.50.1.1)
• PyGLM (version: 0.4.8b1)
• torch (version:1.9.0+cu11)
• pytorch3d (latest)
• Everything else in requirements.txt

Installation

If you need help with installing the dependecies please refer to our installation guide in installation.md.

Running demo

To run our demo, run

python test.py --xml ./released_models/demo.xml --traj ./released_models/demo.txt --model_path ./released_models/model.tar

to visualize our pre-trained control policy for some chopsticks grasping tasks.

Training

To train policies with generated trajectories, run

python train.py --xml 'path_to_MJCF' --traj 'path_of_trajectories' --threads 'num_threads' --logdir 'path_to_save_models'

For example:

python train.py --xml ./data/hand_xml_grasp/demo.xml --traj ./data/traj/ --threads 16 --logdir ./results

You can modify the task_placeholder.py to generate your own training data. Run

python task_placeholder.py --chop_xml ./data/xml_templates/standard/0/chop_kin.xml --hand_xml ./data/xml_templates/standard/0/sim.xml --task_name 'your_task_name' --pose ./data/xml_templates/standard/0/standard.txt

to generate trajectories and MJCF file used for training. The generated motion files will store in ./data/your_task_name/ and the MJCF file will be in ./data/hand_xml_grasp/.