Dexterous Policy

Dexterous policy research focuses on enabling robots to perform complex manipulation tasks using multi-fingered hands, aiming for adaptability and versatility comparable to human dexterity. Current research emphasizes reinforcement learning (RL) approaches, often employing goal-conditioned policies, multi-policy frameworks for handling long-horizon tasks and knowledge transfer, and attention-based mechanisms for efficient learning and policy fusion. This field is significant for advancing robotics capabilities in diverse applications, from manufacturing and surgery to assistive technologies, by enabling robots to handle a wider range of objects and tasks with greater dexterity and efficiency.