Touch-JEPA: Tactile Representation Learning from Hand-Object Video with Limited Tactile Supervision

Minseong Kweon, Qiao Feng, Yiming Huang, Lingjie Liu

Understanding contact and tactile magnitude in hand-object interaction is a key step toward physically intelligent systems, yet learning such representations is limited by the cost and restricted coverage of tactile measurements. We present Touch-JEPA, a framework for learning tactile representations from hand-object videos with limited tactile supervision. We first introduce a self-supervised pretraining objective that learns from video alone without tactile supervision. Using video-derived hand geometry, Touch-JEPA predicts masked visual features at corresponding hand-surface locations from spatial and temporal context, encouraging the representation to capture latent contact dynamics. We then extend this formulation to joint representation learning, where masked prediction is applied to all videos while tactile supervision is available only for a labeled subset, grounding the shared representation in measured contact and magnitude. With self-supervised pretraining alone, simple supervised readouts on frozen Touch-JEPA features outperform supervised baselines in contact discrimination and tactile magnitude estimation across three human-hand benchmarks, and transfer to robotic…