Contact Rich

Contact-rich manipulation focuses on enabling robots to perform tasks requiring intricate physical interaction with objects and environments, emphasizing robust control and safe operation. Current research heavily utilizes reinforcement learning, often incorporating variable impedance control and multi-modal sensor fusion (vision, force, audio) to learn and adapt policies for diverse tasks, with some approaches leveraging differentiable physics simulation and kinematic retargeting for improved efficiency and generalization. This field is crucial for advancing robotics in areas like assembly, human-robot collaboration, and dexterous manipulation, ultimately leading to more capable and adaptable robots in various applications.