English

UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis

Robotics 2026-07-30 v1 Computer Vision and Pattern Recognition

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.

Cite

@article{arxiv.2607.28198,
  title  = {UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis},
  author = {Hui Zhang and Julian Ferchow and Jie Song and Mirko Meboldt},
  journal= {arXiv preprint arXiv:2607.28198},
  year   = {2026}
}

Comments

Project page: https://zdchan.github.io/UniCross/