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

2026-09-02

AI Summary

研究通过结合预测世界模型和PPO策略，解决了人类机器人在脚踏约束地形上的行走问题，提高了跨越障碍的成功率和效率。

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%.

Problem

• Foothold-Constrained Terrain
• Visual Locomotion
• Humanoids

Innovation

• World-Model-Augmented
• Visual Locomotion
• Recurrent World Model
• Foothold-Constrained Terrain
• PPO Policy