Dexterous Manipulation

Dexterous manipulation studies how robots perform precise, contact-rich actions such as in-hand reorientation, tool use, assembly, and handling deformable objects. Approaches combine hand and sensor design with analytical grasp planning, tactile and visual feedback, imitation learning from teleoperation or human motion, and reinforcement learning in simulation. Researchers use benchmarks such as DexGraspNet and task suites in MuJoCo to test grasp quality, policy performance, and generalization across objects and hand designs. A central challenge is transferring policies to real hardware and unfamiliar conditions, where contact, friction, sensing, and embodiment differences can undermine reliability; standardized evaluations remain important for comparing progress.