Contact Rich Manipulation

Contact-rich manipulation focuses on enabling robots to dexterously interact with objects through multiple simultaneous contacts, mirroring human dexterity. Current research emphasizes developing robust planning and control algorithms, often combining data-driven methods like reinforcement learning and diffusion models with classical approaches such as trajectory optimization and search, to handle the high dimensionality and hybrid nature of contact dynamics. These advancements leverage various sensor modalities, including vision and tactile feedback, and aim to improve both the efficiency and reliability of robot manipulation in complex tasks. The resulting improvements in robotic dexterity have significant implications for applications ranging from industrial assembly to surgical robotics and human-robot collaboration.