Robot Learning

Continuous Control

Continuous control is the class of control and reinforcement learning problems whose action spaces are continuous vectors, such as joint torques, velocities, or end-effector twists, rather than discrete choices. It demands policy representations and RL algorithms that handle infinite action sets, driving the development of methods like DDPG, TD3, SAC, and PPO, commonly benchmarked on MuJoCo locomotion suites and DeepMind Control.

Why it matters for physical AI

Virtually all robot motor commands are continuous, so advances in continuous-action policy learning translate directly into what robots can physically execute, from locomotion gaits to dexterous finger coordination.