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DexMimic advances robotics by introducing a generalist policy that learns diverse hand-object manipulation skills from human interaction data, overcoming the limitations of specialist trajectory-based training. The core innovation lies in its Value-Guided Reset Allocation mechanism, which dynamically optimizes initialization states to improve tracking efficiency and enable zero-shot generalization to unseen objects. This work represents a significant step toward scalable, adaptable dexterous robots capable of handling complex real-world tasks.
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation
arXiv:2610.19089v1 Announce Type: new Abstract: Achieving human-like dexterous manipulation remains a central challenge in robotics. Learning from human hand-object interaction (HOI) datasets offers a promising pathway to diverse dexterous skills, yet existing physics-based approaches train specialist policies for individual trajectories, thereby limiting skill reuse at scale. We introduce DexMimic, a generalist dexterous hand-object tracking policy jointly trained across diverse HOI trajectories. Efficient joint training requires allocating each rollout to both an undertrained phase and a starting state that supports continued tracking. DexMimic realizes this insight through Value-Guided Reset Allocation, which adapts the initialization distribution online to prioritize difficult phases and select reset states with higher expected tracking returns. We further introduce a benchmark spanning training, fine-tuning, and generalization. Experiments show improved tracking success and training efficiency, faster fine-tuning, and the potential for zero-shot generalization to unseen trajectories and objects; larger training sets further improve efficiency and generalization. Finally, we validate DexMimic on a real-world dexterous manipulation platform.
Abstract:Achieving human-like dexterous manipulation remains a central challenge in robotics. Learning from human hand-object interaction (HOI) datasets offers a promising pathway to diverse dexterous skills, yet existing physics-based approaches train specialist policies for individual trajectories, thereby limiting skill reuse at scale. We introduce DexMimic, a generalist dexterous hand-object tracking policy jointly trained across diverse HOI trajectories. Efficient joint training requires allocating each rollout to both an undertrained phase and a starting state that supports continued tracking. DexMimic realizes this insight through Value-Guided Reset Allocation, which adapts the initialization distribution online to prioritize difficult phases and select reset states with higher expected tracking returns. We further introduce a benchmark spanning training, fine-tuning, and generalization. Experiments show improved tracking success and training efficiency, faster fine-tuning, and the potential for zero-shot generalization to unseen trajectories and objects; larger training sets further improve efficiency and generalization. Finally, we validate DexMimic on a real-world dexterous manipulation platform.
13 pages, including 4 pages of supplementary material. Project page: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.19089 [cs.RO]
(or arXiv:2610.19089v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.19089 Focus to learn more arXiv-issued DOI via DataCite
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