Locomotion

Locomotion Policy

A locomotion policy is a learned controller, typically a neural network trained with reinforcement learning in simulation, that maps proprioceptive and sometimes exteroceptive observations to joint targets for walking, running, or climbing. Trained with domain randomization and GPU-parallel simulators such as Isaac Gym, and often distilled through teacher-student schemes, such policies have achieved robust rough-terrain quadruped and humanoid locomotion, as demonstrated on ANYmal.

Why it matters for physical AI

Learned locomotion is the clearest existence proof that massively parallel simulation training transfers to real dynamics, establishing the sim-to-real playbook now being applied to manipulation and humanoid whole-body control.