Dexterous in Hand Manipulation

Dexterous in-hand manipulation focuses on enabling robots to skillfully reorient and reposition objects within their grasp, mirroring human dexterity. Current research emphasizes learning-based approaches, employing reinforcement learning with various reward structures (sparse, dense, multi-agent) and model predictive control, often incorporating tactile and visual feedback for improved robustness and generalization across object shapes. These advancements are crucial for improving robotic manipulation in diverse applications, ranging from manufacturing and assembly to assistive technologies. The development of novel robotic hand designs, such as soft robotic hands, also plays a significant role in achieving more human-like dexterity.