Robot Hand

Robot hand research aims to create robotic hands capable of dexterous manipulation comparable to human hands, focusing on tasks like grasping, in-hand manipulation, and object rotation. Current research heavily utilizes reinforcement learning, often incorporating visual and tactile feedback, and employs model architectures such as graph convolutional networks and Gaussian processes to improve control and robustness. These advancements are significant for robotics, enabling more versatile robots for applications ranging from manufacturing and surgery to assistive technologies and household tasks.