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Learning Generalizable Dexterous Manipulation from Human Grasp Affordance

[Project Page] [Paper]

Learning Generalizable Dexterous Manipulation from Human Grasp Affordance,Yueh-Hua Wu*, Jiashun Wang*, Xiaolong Wang, CoRL 2022.

ILAD is a novel pipeline for learning dexterous manipulation from human affordance. This repo contains the imitation learning code for ILAD.

DexMV Teaser

Bibtex

@misc{wu2022ILAD,
  title={Learning Generalizable Dexterous Manipulation from Human Grasp Affordance},
  author={Wu, Yueh-Hua and Wang, Jiashun and Wang, Xiaolong},
  year={2022},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
  }

Installation

  1. Install the MuJoCo. It is recommended to use conda to manage python package:

Install MuJoCo from: http://www.mujoco.org/ and put your MuJoCo licence to your install directory. If you already have MuJoCo on your computer, please skip this step.

  1. Install Python dependencies Create a conda env with all the Python dependencies.
# Download the code from this repo for the simulated environment, retargeting and examples
git clone https://github.com/kristery/Learning-Generalizable-Dexterous-Manipulation-from-Human-Grasp-Affordance.git
cd Learning-Generalizable-Dexterous-Manipulation-from-Human-Grasp-Affordance

# The provoided package version in the yml is our testing environment, you do not need to follow the version of each python package precisely to run this code.
conda env create -f environment.yaml 
conda activate ilad
pip install -e mj_envs/
pip install -e mjrl/
python setup.py develop

cd hand_imitation
python setup.py develop
cd ..

cd dapg/dapg/demonstrations/
pip install gdown
sh ./download.sh
cd ../../../
  1. The file structure is listed as follows:

exp_cfg/: training configurations for ILAD, DAPG, and RL

mjrl/: Implementation of algorithms

dapg/: Demonstrations for training ILAD and DAPG

hand_imitation: training environments

tools/main.py: main script for training

Training

Download processed demonstrations

Training config

We use a config system to specify the training parameters, including task, object, imitation learning algorithm or RL only. For convenience, we have provided several config files for you in exp_cfg/.

Training

For example, if you want to train relocate mug task using ILAD with demonstrations:

python tools/main.py --cfg exp_cfg/shapenet_relocate_mug_ilad_cfg.yaml

Similarly, there are several config files for other tasks and algorithms in the exp_cfg directory you can use for training.

Acknowledge

Some file in this repository is modified based on the code from soil, dapg, dexmv-sim. We gratefully thank the authors of these amazing projects.