SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 50 of its 52 references that are in the map. First posted on arXiv 4 Aug 2026.
Builds on
50 of 52 references.
References · 50
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38 more
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Citing papers per year: 2026: 2.
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- 2025In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data
- 2025OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation
- 2026Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data
- 2026Wh0: Generative World Models as Scalable Sources of Egocentric Human Hand Manipulation Data
- 2026IronMind: Scaling Humanoid Dexterous Manipulation via Camera-Space Ego-Centric Pretraining ↗
- 2026EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
- 2026Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
- 2026H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models