Elizaveta Kovtun

The work introduces Tactile-JEPA, a self-supervised pretraining method for distributed tactile sensors (e-skins) that samples masks at local and global scales over the taxel connectivity graph and predicts the embeddings of masked taxels; across magnetic and piezoresistive sensors and three datasets (Sparsh-skin, Tactile socks, DECO-50) it reduces force-estimation RMSE by 6.3% and in-hand pose RMSE by 20.8% over the strongest prior baseline, with consistent gains in action classification, object classification, and tactile-conditioned policy learning.

The work introduces Tactile-JEPA, a self-supervised pretraining method for distributed tactile sensors (e-skins) that samples masks at local and global scales over the taxel connectivity graph and predicts the embeddings of masked taxels; across magnetic and piezoresistive sensors and three datasets (Sparsh-skin, Tactile socks, DECO-50) it reduces force-estimation RMSE by 6.3% and in-hand pose RMSE by 20.8% over the strongest prior baseline, with consistent gains in action classification, object classification, and tactile-conditioned policy learning.