RL for Legged Locomotion
RL: Reinforcement Learning
Overview
This field studies how quadruped and humanoid robots can learn coordinated walking, running, jumping, and navigation while staying balanced and managing contact with the ground. Common approaches train actor–critic policies such as PPO in simulation, using reward shaping and curricula; others add motion-imitation priors, perception, learned dynamics, or model-predictive control. Researchers test policies in simulators and on hardware across settings such as stairs, gaps, rough or deformable terrain, payloads, and external pushes, often using teacher–student distillation to transfer privileged simulation training to real sensors. A central challenge is achieving reliable, safe sim-to-real transfer and broad terrain or morphology generalization without sacrificing efficiency, natural motion, or energy use.
Latest papers
166 papers33 papers in the last four weeks, up 106% on the four weeks before. 0.2% of all new papers.