Robust Manipulation

Robust manipulation in robotics aims to enable robots to reliably grasp, move, and interact with objects despite uncertainties in the environment and object properties. Current research focuses on developing robust control policies, often using reinforcement learning and optimization techniques, alongside improved end-effector designs (e.g., multi-suction cup grippers) and advanced scene understanding methods (e.g., leveraging visual and auditory feedback, diffusion models). These advancements are crucial for expanding the capabilities of robots in unstructured environments, improving efficiency and reliability in tasks like assembly, pick-and-place, and teleoperation.