EgoTac: In-the-wild Tactile Prediction from Egocentric Vision
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 data...
| Authors | Wenkang Zhang, Chengbo Yuan, Zicheng Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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