2026
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
RA-L 2026
Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such multisensory settings, especially amidst sensory noise and dynamic changes. We propose MultiSensory Dynamic Pretraining