UltraDexGrasp

KO | EN

gitlite — search

#llm #go #android #deep-learning #linux #docker #windows #awesome #vue #awesome-list #security #mcp #openai #open-source

(★ 87)

[ICRA 2026] UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data

UltraDexGrasp 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

<br>
<p align="center">
  <h1 align="center"><strong>UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data</strong></h1>
  <p align="center">
    Sizhe Yang, Yiman Xie, Zhixuan Liang, Yang Tian, Jia Zeng, Dahua Lin, Jiangmiao Pang
    <br>
    Shanghai AI Laboratory, The Chinese University of Hong Kong, Zhejiang University, The University of Hong Kong, Peking University
    <br>
  </p>

[![arXiv](https://img.shields.io/badge/arXiv-2603.05312-green)](https://arxiv.org/abs/2603.05312)
[![Homepage](https://img.shields.io/badge/Homepage-%F0%9F%8C%90-blue)](https://yangsizhe.github.io/ultradexgrasp/)

</div>

## 📋 Contents

- [🔥 Highlight](#highlight)
- [🛠️ Getting Started](#getting_started)
- [🔗 Citation](#citation)
- [📄 License](#license)
- [👏 Acknowledgements](#acknowledgements)

## 🔥 Highlight <a name="highlight"></a>

**UltraDexGrasp** is a framework for universal dexterous grasping with bimanual robots.

The proposed data generation pipeline integrates an optimization-based grasp synthesizer with a planning-based demonstration generation module, and **supports multiple grasp strategies, including two-finger pinch, three-finger tripod, whole-hand grasp, and bimanual grasp**.

Trained on data produced by this pipeline, the policy demonstrates **robust zero-shot sim-to-real transfer and strong generalization to novel objects** with varied shapes, sizes, and weights.

## 🛠️ Getting Started <a name="getting_started"></a>

### Installation

First, clone this repository.

```
git clone https://github.com/InternRobotics/UltraDexGrasp.git
```

(Optional) Use conda to manage the python environment.

```
conda create -n ultradexgrasp python=3.10 -y
conda activate ultradexgrasp
```

Install dependencies.
```
# Install PyTorch according to your CUDA version. For example, if your CUDA version is 11.8:
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118

# Install other dependencies
pip install -r requirements.txt

mkdir third_party; cd third_party

# Install PyTorch3D. If you encounter any problems, please refer to the detailed installation instructions at https://github.com/facebookresearch/pytorch3d.
git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d
pip install -e . --no-build-isolation
cd ..

# Install cuRobo
sudo apt install git-lfs
git clone https://github.com/NVlabs/curobo.git
cd curobo
pip install -e . --no-build-isolation
cd ..

# Install BODex_api (adapted from https://github.com/JYChen18/BODex)
git clone https://github.com/yangsizhe/BODex_api.git
cd BODex_api
pip install -e . --no-build-isolation
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.4.1+cu118.html  # for torch-2.4.1 and cuda-11.8
conda install coal -c conda-forge -y
conda install boost=1.84.0 -y
cd src/bodex/geom/cpp; python setup.py install
cd ../..

```

### Usage

Generate trajectories for various grasp strategies by running:

```
# left whole hand grasp
python rollout.py \
    --hand 0 \
    --object_scale_list '[0.08]'

# right whole hand grasp
python rollout.py \
    --hand 1 \
    --object_scale_list '[0.08]'

# bimanual grasp
python rollout.py \
    --hand 2 \
    --object_scale_list '[0.25]'

# left three-finger tripod
python rollout.py \
    --hand 3 \
    --object_scale_list '[0.04]'

# right three-finger tripod
python rollout.py \
    --hand 4 \
    --object_scale_list '[0.04]'

```

You can change the `object_mesh_path` in `env/config/env.yaml` to the mesh path of another object. For mesh processing, please refer to [BODex](https://github.com/JYChen18/BODex).

## 🔗 Citation <a name="citation"></a>

If you find our work helpful, please cite:

```bibtex
@article{yang2026ultradexgrasp,
  title={UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data},
  author={Yang, Sizhe and Xie, Yiman and Liang, Zhixuan and Tian, Yang and Zeng, Jia and Lin, Dahua and Pang, Jiangmiao},
  journal={arXiv preprint arXiv:2603.05312},
  year={2026}
}
```

## 📄 License <a name="license"></a>

This repository is released under the [Apache 2.0 license](./LICENSE).

## 👏 Acknowledgements <a name="acknowledgements"></a>

Our code is built upon [BODex](https://github.com/JYChen18/BODex). We thank the authors for open-sourcing their code and for their significant contributions to the community.

UltraDexGrasp

UltraDexGrasp 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

UltraDexGrasp

UltraDexGrasp 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README