arXiv AI
Learning Tactile-Aware Hand Representations from Human Hand-Object Interaction Data
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Touch-JEPA: Tactile Representation Learning from Hand-Object Video with Limited Tactile Supervision
Touch-JEPA is a framework that learns tactile representations from hand‑object videos using limited tactile supervision. It first trains a self‑supervised model that predicts masked visual features at hand‑surface locations, capturing latent contact dynamics. The model is then jointly fine‑tuned on a small labeled subset, improving contact discrimination and tactile magnitude estimation on human‑hand benchmarks and transferring to robotic hands without robot tactile data.
arXiv Computer Vision
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TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning
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DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.
arXiv Machine Learning
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Temporal Visuo-Tactile Learning for Dexterous Grasp Stability
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