Hand Arm System

Hand-arm system research focuses on enabling robots to perform dexterous manipulation tasks requiring coordinated arm and hand movements. Current efforts concentrate on developing robust control policies, often using modular neural networks or deep reinforcement learning with population-based training, to achieve complex actions like object catching, grasping, and bimanual tasks such as stirring. These advancements leverage differentiable neural distance functions for efficient grasp synthesis and address challenges in high-dimensional control spaces and long-horizon task decomposition. The resulting improvements in robotic dexterity have significant implications for human-robot collaboration, object manipulation in industrial settings, and assistive robotics.