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arXiv · 2608.04196

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

Nie Lin·Takehiko Ohkawa·Sijin Chen·Ruoshi Wen·Zhuohang Li·Liqun Huang·Zhengming Zhu·Yiming Bao·Yunfei Li·Minjie Cai·Xiao Ma·Wei Xu·Yoichi Sato
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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.

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Nie Lin, Takehiko Ohkawa, Sijin Chen, Ruoshi Wen, Zhuohang Li, Liqun Huang, Zhengming Zhu, Yiming Bao, Yunfei Li, Minjie Cai, Xiao Ma, Wei Xu, Yoichi Sato. 2026-08-04. SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation. https://arxiv.org/abs/2608.04196

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