Tactile ExplorationExpert
Tactile Curiosity Drives Robot Interaction
This preprint presents TacEx, a framework that directs reinforcement-learning exploration by weighting tactile predictive uncertainty instead of total state uncertainty. In simulation, tactile-driven exploration produces contact-rich examples, emergent grasping without task rewards, and improved offline pick-and-place and frozen VLA post-training. It matters because it makes manipulation data collection and VLA adaptation far more sample-efficient by focusing learning where contact occurs.