PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
CVPR 2023
This is the official repository of PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations.
For more information, please visit our project page.
We introduce a large-scale cross-category part manipulation benchmark PartManip with diverse object datasets, realistic settings, and rich annotations. We propose a generalizable vision-based policy learning strategy and boost the performance of part-based object manipulation by a large margin, which can generalize to unseen object categories and novel objects in the real world.
We first train state-based expert policy using our proposed canonicalization to the part coordinate frame and the part-aware reward. We then use the learned expert to collect demonstrations for pre-training the vision-based policy by behavior cloning. After pre-training, we train the vision-based policy to imitate the state-based expert policy using DAgger. We also introduce several point cloud augmentation techniques to boost the generalization ability. For the vision backbone, we introduce 3D Sparse-UNet which has a large expression capability. Furthermore, we introduced an extra domain adversarial learning module for better cross-category generalization.
To obtain our assets, please fill out this form and check the Terms&Conditions in it. Please cite our paper if you use our dataset.
-
install issacgym following the official guide
-
install dependencies
pip install wandb tensorboard ipdb gym tqdm rich opencv_python pytorch3d pyparsing pytorch_lightning addict yapf h5py sorcery pynvml torchdata==0.5.1 einops
- (Optional for full implementation, not necessary) install GAPartNet environment following the official guide
For training:
run scripts in scripts/train.sh
to train different tasks.
An example:
# drawer
python gym/train.py --task=FrankaPoseCabinetBase --algo=pregrasp_ppo \
--task_config=cfg/task_cfg.yaml --algo_config=cfg/algo/pregrasp_ppo.yaml \
--sim_device=cuda:0 --graphics_device_id=0 --seed=0526 --headless \
--group_name state --save_name 0.2_2_0_1_0_2 \
--rot_coef 0.2 --handle_coef 2 --vel_coef 0 --dist_coef 1 --tip_coef 0 --dicouple_coef 2 \
--target open --category drawer --minibatches 2 --nsteps 5 --noptepochs 8 \
--canonical_space handle --pregrasp part --obs state --eval_round 3 --eval_freq 10 \
--action_normalization clip --control ik_abs_axis_angle --asset_num 200_40_40_5
For testing:
run scripts in scripts/test.sh
to test different tasks with trained ckpt.
An example:
# drawer
python gym/train.py --task=FrankaPoseCabinetBase --algo=pregrasp_ppo \
--task_config=cfg/task_cfg.yaml --algo_config=cfg/algo/pregrasp_ppo.yaml \
--sim_device=cuda:0 --graphics_device_id=0 --seed=0526 --headless \
--group_name state --save_name 0.2_2_0_1_0_2 \
--rot_coef 0.2 --handle_coef 2 --vel_coef 0 --dist_coef 1 --tip_coef 0 --dicouple_coef 2 \
--target open --category drawer --minibatches 2 --nsteps 5 --noptepochs 8 \
--canonical_space handle --pregrasp part --obs state --eval_round 3 --eval_freq 10 \
--action_normalization clip --control ik_abs_axis_angle --asset_num 200_40_40_5 \
--test --ckpt xxxx.tar
There are some examples of our tested training curves:
A training example of drawer:
A training example of door:
This work and the dataset are licensed under CC BY-NC 4.0.
If you find our work useful in your research, please consider citing:
@article{geng2023partmanip,
title={PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations},
author={Geng, Haoran and Li, Ziming and Geng, Yiran and Chen, Jiayi and Dong, Hao and Wang, He},
journal={arXiv preprint arXiv:2303.16958},
year={2023}
}
If you have any questions, please open a github issue or contact us:
Haoran Geng: [email protected]
He Wang: [email protected]