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ManipTrans

Robotics
PyTorch
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  [![Paper](https://img.shields.io/badge/Paper-red?style=for-the-badge&labelColor=B31B1B&color=B31B1B)](https://arxiv.org/abs/2503.21860)
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  [![Project](https://img.shields.io/badge/Project-orange?style=for-the-badge&labelColor=D35400)](https://maniptrans.github.io/)
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- [![Dataset (uploading)]](https://img.shields.io/badge/Dataset%20(uploading)-orange?style=for-the-badge&labelColor=FFD21E&color=FFD21E)
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  This model is described in the paper [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://huggingface.co/papers/2503.21860). It's a two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. The model first pre-trains a generalist trajectory imitator and then fine-tunes a specific residual module.
 
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  [![Paper](https://img.shields.io/badge/Paper-red?style=for-the-badge&labelColor=B31B1B&color=B31B1B)](https://arxiv.org/abs/2503.21860)
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  [![Project](https://img.shields.io/badge/Project-orange?style=for-the-badge&labelColor=D35400)](https://maniptrans.github.io/)
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+ [![Dataset](https://img.shields.io/badge/Dataset-orange?style=for-the-badge&labelColor=FFD21E&color=FFD21E)](https://huggingface.co/datasets/LiKailin/DexManipNet)
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  This model is described in the paper [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://huggingface.co/papers/2503.21860). It's a two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. The model first pre-trains a generalist trajectory imitator and then fine-tunes a specific residual module.