Dexterous Manipulation

Dexterous manipulation refers to fine, multi-fingered manipulation tasks that exploit the full kinematic and sensory capabilities of a robotic hand — in-hand regrasping, rolling objects across fingertips, card dealing, surgical suturing, and similar tasks. Dexterity requires high-DOF end-effectors (5+ fingers, each with 3+ joints), dense tactile sensing, and policies capable of reasoning about complex contact geometry. Reinforcement learning trained in simulation (e.g., OpenAI's Dactyl) and recent diffusion-based policies have pushed the frontier, but dexterous manipulation at human-level reliability remains an open research problem.