arXiv:2606.10683v1 Announce Type: cross Abstract: Dexterous hands are essential for fine-grained manipulation, but their hardware designs vary substantially across embodiments. Differences in kinematics, joint definitions, and degrees of freedom make it difficult to define a shared state representation compared with parallel grippers. As a result, dexterous-hand data remains fragmented and difficult to use for joint training. In this work, we propose the Unified Dexterous Hand Model (UDHM), which maps human and robot hand states into a shared 22-DoF semantic interface. Based on UDHM, we introd
The proliferation of various dexterous hand designs in robotics creates a pressing need for a unified data representation to advance manipulation capabilities, coinciding with increased investment in robotics research.
This work addresses a core fragmentation problem in dexterous manipulation, which is a bottleneck for developing more general and capable robotic systems essential for various industries.
A shared semantic interface for dexterous hand states will enable more efficient data utilization, cross-platform learning, and accelerate progress in robotic manipulation beyond parallel grippers.
- · Robotics research labs
- · Dexterous robotics manufacturers
- · AI reinforcement learning developers
- · Logistics and manufacturing sectors
- · Developers relying on proprietary, non-standardized dexterous hand data
- · Hardware-centric robot design approaches without software abstraction
The Unified Dexterous Hand Model (UDHM) provides a standardized way to represent and process data from diverse robotic hands, fostering collaboration and data sharing.
This standardization will accelerate the development of more capable and generalizable manipulation policies for complex tasks, moving beyond current specialized solutions.
Improved dexterous manipulation will enable robots to perform more human-like tasks in unstructured environments, significantly broadening their applications in service industries and hazardous work.
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Read at arXiv cs.AI