arXiv Computer Vision By Hui Zhang, Julian Ferchow, Jie Song, Mirko Meboldt

UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis

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UniCross proposes a unified framework that models the four canonical dexterous manipulation skills—grasping, relocation, in-hand rotation, and in-hand translation—within a single formulation sharing state, action spaces, and objective structure. This approach enables distillation of a single cross-skill policy that performs well across all skills, generalizes to unseen objects, remains robust to disturbances, and can chain skills into long-horizon manipulation without policy switching. The framework also transfers effectively across different hand morphologies, suggesting that these skills can be viewed as instantiations of a shared task formulation.

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arXiv AI
Jul 29

InDex: Empowering VLA Models with Intent-Conditioned Arm-Hand Coordination for Dexterous Manipulation

arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.

By Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding
arXiv AI
3d ago

GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive

GOTT is a reach‑acquire‑move framework that uses a single cross‑embodiment contact‑acquisition primitive to enable dexterous manipulation across different robot embodiments. The system first positions the hand near a task‑relevant contact region, then the shared closed‑loop primitive establishes stable contact, after which a pose‑conditioned controller tracks the desired object motion. Experiments in simulation and on real robots demonstrate that GOTT reliably establishes robust contact across diverse objects, arm‑hand platforms, and both seen and unseen hand morphologies, and consistently improves end‑to‑end task success compared to open‑loop grasp execution.

By Yulin Liu, Lai Wei, Yen-Jen Wang, Akash Sharma, Pieter Abbeel, Henrik I. Christensen, Haozhi Qi