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This research introduces a unified framework for dexterous robotic manipulation that integrates grasping, relocation, rotation, and translation into a single policy formulation. By sharing state spaces and objectives across these skills, the approach enables seamless chaining of actions for long-horizon tasks while maintaining robustness to disturbances and generalizing effectively across unseen objects and hand morphologies. The work advances robotics by demonstrating intrinsic consistency among different manipulation behaviors through shared task formulations rather than isolated skill modeling.
UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis
arXiv:2607.28198v1 Announce Type: new Abstract: Many dexterous manipulation tasks require the object to remain securely held throughout the interaction. From the perspective of hand-object relational motion, such manipulation comprises four canonical skills: grasping, relocation, in-hand rotation, and in-hand translation. Human hands flexibly compose these skills to accomplish complex tasks. Existing approaches, however, model these skills separately with skill-specific action constraints, objectives, or even dedicated hand morphologies, which breaks the compatibility and continuity required for long-horizon composition. In this work, we present a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure. This formulation enables straightforward distillation of a single cross-skill policy that performs strongly on every skill, generalizes to unseen objects, stays robust to disturbances, and chains skills seamlessly into long-horizon manipulation. The framework also transfers effectively across different hand morphologies. Overall, our results suggest that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consistency across different behaviors.
Abstract:Many dexterous manipulation tasks require the object to remain securely held throughout the interaction. From the perspective of hand-object relational motion, such manipulation comprises four canonical skills: grasping, relocation, in-hand rotation, and in-hand translation. Human hands flexibly compose these skills to accomplish complex tasks. Existing approaches, however, model these skills separately with skill-specific action constraints, objectives, or even dedicated hand morphologies, which breaks the compatibility and continuity required for long-horizon composition. In this work, we present a unified framework that models all four skills in a single formulation that shares the same state and action spaces and a common objective structure. This formulation enables straightforward distillation of a single cross-skill policy that performs strongly on every skill, generalizes to unseen objects, stays robust to disturbances, and chains skills seamlessly into long-horizon manipulation. The framework also transfers effectively across different hand morphologies. Overall, our results suggest that different dexterous manipulation skills can be viewed as instantiations of a shared task formulation, revealing the intrinsic consistency across different behaviors.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2607.28198 [cs.RO]
(or arXiv:2607.28198v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.28198 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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