Hand Object Rotation

Hand object rotation research focuses on enabling robots to dexterously manipulate objects within their grasp, mirroring human capabilities. Current efforts concentrate on developing robust control algorithms, often employing model predictive control or reinforcement learning, and integrating diverse sensory modalities like vision and tactile sensing to improve accuracy and adaptability. These advancements leverage techniques such as contact-implicit modeling and visuotactile fusion to achieve reliable in-hand manipulation across a range of object shapes and properties, paving the way for more versatile and adaptable robotic systems in manufacturing and other fields.