UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data

The paper introduces UniDexTok, a retargeting-free state tokenizer that utilizes a Unified Dexterous Hand Model (UDHM) to create a shared 22-DoF semantic interface for dexterous hands. UniDexTok achieves significant performance improvements over the baseline UniHM, reducing the Mean Per Joint Angle Error (MPJAE) from 15.63 degrees to 0.16 degrees and the Mean Per Joint Position Error (MPJPE) from 18.51 mm to 0.18 mm, enhancing reconstruction accuracy from centimeter-scale to sub-millimeter. This advancement is crucial for practitioners as it enables more effective cross-embodiment learning and reduces reliance on simulation data, facilitating better integration of diverse dexterous hand designs in robotic applications.