DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation

We study the problem of functional retargeting: learning dexterous manipulation policies to track object states from human hand-object demonstrations. We focus on long-horizon, bimanual tasks with articulated objects, which is challenging due to large action space, spatiotemporal discontinuities, and embodiment gap between human and robot hands. We propose DexMachina, a novel curriculum-based algorithm: the key idea is to use virtual object controllers with decaying strength: an object is first driven automatically towards its target states, such that the policy can gradually learn to take over under motion and contact guidance. We release a simulation benchmark with a diverse set of tasks and dexterous hands, and show that DexMachina significantly outperforms baseline methods. Our algorithm and benchmark enable a functional comparison for hardware designs, and we present key findings informed by quantitative and qualitative results. With the recent surge in dexterous hand development, we hope this work will provide a useful platform for identifying desirable hardware capabilities and lower the barrier for contributing to future research. Videos and more atthis https URL

@article{mandi2025_2505.24853,
  title={ DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation },
  author={ Zhao Mandi and Yifan Hou and Dieter Fox and Yashraj Narang and Ajay Mandlekar and Shuran Song },
  journal={arXiv preprint arXiv:2505.24853},
  year={ 2025 }
}