EgoTac: 自己中心視覚からの実環境触覚予測
EgoTac: In-the-wild Tactile Prediction from Egocentric Vision
自己中心視覚映像から触覚情報を予測するモデルEgoTacを提案し、570万以上の画像-触覚ペアで学習して高精度な触覚予測を実現した。
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著者: Wenkang Zhang, Chengbo Yuan, Zicheng Zhang, Zhengxue Cheng, Yang Gao
分類: cs.CV, cs.RO
原文アブストラクト
Touch is fundamental to dexterous manipulation, yet most egocentric human data increasingly used for robot learning lacks tactile information. Directly collecting large-scale tactile data is challenging due to sensor limitations, while human video data is abundant, contact-rich, and easily scalable. This motivates a natural question: can tactile signals be inferred purely from vision? To address this, we introduce EgoTac, a generalizable model that predicts rich tactile information directly from egocentric human videos. EgoTac is trained on a unified corpus of over 5.7M image-tactile pairs, covering both continuous force measurements and binary contacts. By learning from this diverse dataset, EgoTac captures nuanced touch dynamics across varied interactions. Experiments demonstrate strong performance: in-domain prediction achieves an average force error below 0.06N. On out-of-domain contact prediction benchmarks, EgoTac consistently outperforms the state-of-the-art contact estimator. It also captures the rise and fall patterns of real tactile data and enables zero-shot predictions on unconstrained real-world videos. Scaling analyses further reveal that both data diversity and volume improve performance steadily. Overall, EgoTac provides a scalable pathway to extract tactile priors from egocentric human videos, enabling broadly applicable tactile-aware robot learning.