World-Model-Augmented Visual Locomotion for Humanoids on Foothold-Constrained Terrain

Abstract:Foothold-constrained terrain is characterized by sparse, discontinuous, or geometrically restricted feasible foot contacts, as encountered on stepping stones, across gaps, and on narrow stair treads. On such terrain, a single misstep often leaves little room to recover, so policies that base foot-placement decisions primarily on the immediately visible terrain are prone to failure. We ask whether a learned predictive summary of near-future observations and rewards can provide the anticipatory information required in such settings. We present World-Model-Augmented Visual Locomotion (WM-LOCO), which jointly trains a recurrent world model and a PPO policy. Conditioned on proprioception and a single onboard depth image, the world model produces a predictive recurrent feature that guides the policy, without explicit foothold labels. In simulation, WM-LOCO succeeds on gaps and stepping stones where a matched baseline fails completely, and matches the baseline's success rate on stairs while improving stride efficiency and reducing pelvis acceleration. We deploy the same policy onboard a physical Unitree G1 humanoid using onboard proprioception and a single depth stream; it traverses all three terrain classes with an average success rate of 93.3%.

11 pages, 3 figures, 4 tables. Yuxi Liu and Lijun Han contributed equally

Robotics (cs.RO)

arXiv:2609.02542 [cs.RO]

(or arXiv:2609.02542v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.02542 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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