Angular Momentum LIP Guided Reinforcement Learning With Step Duration Modulation for Dynamic Locomotion of Bipedal Robots

Abstract

Reinforcement learning shows strong potential for bipedal locomotion control, while reduced-order models provide compact and interpretable physical priors. This paper presents an Angular Momentum Linear Inverted Pendulum (ALIP) guided reinforcement learning framework for dynamic locomotion of a small point-foot bipedal robot, where foot placement references generated online by ALIP serve as structured physical guidance for policy training. Compared with end-to-end learning without reference guidance and a counterpart guided by the conventional linear inverted pendulum based model under matched training settings, ALIP guidance shows improved center-of-mass height regulation and enhanced robustness to payload and unseen-terrain disturbances. Furthermore, a speed-conditioned step duration modulation mechanism constrained by ALIP gait parameters and hardware-feasibility is introduced to raise the maximum achievable speed and improve speed tracking accuracy. The framework is validated in both simulation and hardware experiments, where the robot achieves a maximum forward speed of $2.26 \,\mathrm{m}\mathrm{/}\mathrm{s}$ (Froude number 1.30) and stable locomotion with a payload of $37.5 \,\%$ of the nominal robot mass, with additional qualitative demonstrations of stable traversal across diverse unseen terrains.