Hand Manipulation

Hand manipulation in robotics aims to enable robots to dexterously manipulate objects within their grasp, mirroring human dexterity. Current research heavily focuses on leveraging reinforcement learning (RL), often combined with tactile sensing and various neural network architectures (e.g., graph neural networks), to learn robust and adaptable manipulation policies, often transferring skills from simulation to real-world scenarios. This field is crucial for advancing robotics in areas like manufacturing, surgery, and assistive technologies, as it addresses the limitations of traditional robotic manipulation methods in handling complex, unstructured environments. The development of more efficient and generalizable algorithms, along with improved tactile sensing, are key objectives driving ongoing research.