SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
Nie LinTakehiko OhkawaSijin ChenRuoshi WenZhuohang LiLiqun HuangZhengming ZhuYiming BaoYunfei LiMinjie CaiXiao MaWei XuYoichi Sato
Oct 2026
Machine LearningComputer VisionRobotics
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 runs a three-stage recall-ranking-re-ranking cascade over ~32M egocentric human samples: recall and ranking select a task-relevant subset in a morphology-agnostic action space, and re-ranking verifies it on pixels, with no change 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 61.7%, showing that selective curation outperforms indiscriminate data mixing.
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