Researchers submitted a paper introducing H-Tac, a large-scale tactile-action dataset with 160 hours of egocentric human videos containing over 300 tasks and 135,000 episodes, paired with a Transferable Tactile Pre-Training (TTP) framework for robotic manipulation.
The dataset addresses critical limitations in existing tactile sensing systems, which suffer from small scale and narrow contact coverage; H-Tac's scale and task diversity enable larger-scale tactile-based pre-training compared to current alternatives.
The TTP framework preserves human knowledge during robot transfer by using unified tactile and action spaces across pre-training and post-training phases, with a tactile expert for future tactile prediction to improve performance on downstream dexterous tasks.