Touch-based Curiosity for Sparse-Reward Tasks

Understand

Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.

• In this work, we leverage surprise from mismatches in touch feedback to guide exploration in hard sparse-reward reinforcement learning tasks.
• Our approach, Touch-based Curiosity (ToC), learns what visible objects interactions are supposed to "feel" like.
• We encourage exploration by rewarding interactions where the expectation and the experience don't match.