TerAdapt: Proprioceptive Terrain-Adaptive Locomotion via Codebook Aligned Representation Learning

Abstract

Humanoid robots aim to achieve human-like locomotion in unstructured environments. However, designing a controller for such robots is highly challenging due to their inherent instability and the requirement to adapt to diverse terrains. To address this problem, we present TerAdapt, a proprioceptive terrain-adaptive locomotion framework that learns semantically meaningful terrain representations and delivers terrain-aware gait modulation directly from onboard proprioception. TerAdapt achieves this through Terrain Codebook Alignment (TCA), which discretizes elevation maps into a compact terrain codebook and aligns these semantic terrain tokens with a latent representation inferred purely from proprioceptive history. This alignment enables the policy to infer terrain categories and generate adaptive gaits solely from onboard proprioception. Extensive experiments in simulation and on the Unitree G1 humanoid robot demonstrate that TerAdapt achieves state-of-the-art performance among proprioceptive methods, delivering robust and adaptive locomotion across challenging terrains without any exteroceptive sensing.