SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation

Abstract:Recent years have witnessed an explosive trend of scaling ego-centric human videos for robot manipulation, yet it remains unclear which data actually benefits dexterous manipulation. We present SiMDex, a similarity-based data mining framework that casts human data selection for VLA post-training in dexterous manipulation as a recommendation problem. For each robot demonstration, SiMDex employs a three-layer recall-ranking-re-ranking pipeline to extract task-relevant subsets from a pool of ~32M egocentric human samples, operating in a morphology-agnostic action space that requires no changes to VLA architecture or training. Against a strong baseline trained with an equal amount of randomly sampled human data, SiMDex uses only ~1.49M mined samples (<5% of the pool) yet improves the overall success rate from 47.7% to 61.1%, showing that selective curation outperforms indiscriminate data mixing.

12 pages, 4 figures. Project page: this https URL

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2608.04196 [cs.RO]

(or arXiv:2608.04196v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.04196 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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