Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations
We present Human2Dex, a wrist-centric human demonstration interface that separates shared human observation and motion semantics from embodiment-specific execution.
ProofPaper ↗
Key points
- Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition.
- We ask whether one shared corpus of natural human demonstrations can instead serve as a reusable data interface for different dexterous hands, without collecting task demonstrations on each target robot.
- It constructs executable supervision by retargeting fused human hand motion to each target hand, and introduces the Grasp-Pocket Interaction Representation (GPIR) to express tracked objects relative to a hand-centered Functional Grasp Pocket.
- These results support the use of wrist-centric human demonstrations as a reusable interface across calibrated dexterous hands while retaining embodiment-specific policy learning.
Sources (1)
- [1]Human2Dex: Dexterous Robot Learning from Wrist-Centric Human DemonstrationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 07:14 AM
We present Human2Dex, a wrist-centric human demonstration interface that separates shared human observation and motion semantics from embodiment-specific execution.
Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition.
Extractive summary: sentences quoted from the sources.
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