DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects,

Chen Bao*, Helin Xu*, Yuzhe Qin, Xiaolong Wang, CVPR 2023.

DexArt is a novel benchmark and pipeline for learning multiple dexterous manipulation tasks. This repo contains the simulated environment and training code for DexArt.

[2023.11.21] All the RL checkpoints are available now!🎈 They are included in the assets. See Main Results to reproduce the results in the paper!
[2023.4.18] Code and vision pre-trained models are available now!
[2023.3.24] DexArt is accepted by CVPR 2023! 🎉

1. Clone the repo and Create a conda env with all the Python dependencies.

git clone [email protected]:Kami-code/dexart-release.git
cd dexart-release
conda create --name dexart python=3.8
conda activate dexart
pip install -e .    # for simulation environment
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch    # for visualizing trained policy and training 

2. Download the assets from the Google Drive and place the asset directory at the project root directory.

The file structure is listed as follows:

dexart/env/: environments

assets/: tasks annotations, object, robot URDFs and RL checkpoints

examples/: example code to try DexArt

stable_baselines3/: RL training code modified from stable_baselines3

python examples/random_action.py --task_name=laptop

task_name: name of the environment [faucet, laptop, bucket, toilet]

python examples/visualize_observation.py --task_name=laptop

task_name: name of the environment [faucet, laptop, bucket, toilet]

python examples/visualize_policy.py --task_name=laptop --checkpoint_path assets/rl_checkpoints/laptop/laptop_nopretrain_0.zip

task_name: name of the environment [faucet, laptop, bucket, toilet]

use_test_set: flag to determine evaluating with seen or unseen instances

python3 examples/train.py --n 100 --workers 10 --iter 5000 --lr 0.0001 &&
--seed 100 --bs 500 --task_name laptop --extractor_name smallpn &&
--pretrain_path ./assets/vision_pretrain/laptop_smallpn_fulldata.pth 

n: the number of rollouts to be collected in a single episode

workers: the number of simulation progress

iter: the total episode number to be trained

lr: learning rate of RL

seed: seed of RL

bs: batch size of RL update

task_name: name of training environment [faucet, laptop, bucket, toilet]

extractor_name: different PointNet architectures [smallpn, meduimpn, largepn]

pretrain_path: path to downloaded pre-trained model. [Default: None]

save_freq: save the model every save_freq episodes. [Default: 1]

save_path: path to save the model. [Default: ./examples]

python examples/evaluate_policy.py --task_name=laptop --checkpoint_path assets/rl_checkpoints/laptop/laptop_nopretrain_0.zip --eval_per_instance 100
python examples/evaluate_policy.py --task_name=laptop --use_test_set --checkpoint_path assets/rl_checkpoints/laptop/laptop_nopretrain_0.zip --eval_per_instance 100

task_name: name of the environment [faucet, laptop, bucket, toilet]

use_test_set: flag to determine evaluating with seen or unseen instances

We have uploaded the code to generate a dataset and pretrain our models in examples/pretrain. You can refer to examples/pretrain/run.sh for a detailed usage.

@inproceedings{bao2023dexart,
  title={DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects},
  author={Bao, Chen and Xu, Helin and Qin, Yuzhe and Wang, Xiaolong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21190--21200},
  year={2023}
}

This repository employs the same code structure for simulation environment and training code to that used in DexPoint.