Robot Learning

Reinforcement Learning for Robotics

Reinforcement learning for robotics is the application of trial-and-error learning to robot control, training policies to maximize cumulative reward through interaction. Because real-robot experience is slow, costly, and unsafe, the dominant recipe trains in massively parallel simulation with domain randomization and transfers to hardware. Landmark results include OpenAI's in-hand Rubik's cube manipulation, learned quadruped locomotion over wild terrain, and champion-level autonomous drone racing.

Why it matters for physical AI

Reinforcement learning produces behaviors no one can demonstrate or hand-program, particularly dynamic whole-body skills, making it the complement to imitation learning in the modern robot learning toolbox.