Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction

Abstract:Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-based gait generators yield interpretable motions but often lack the robustness and adaptability of modern reinforcement learning (RL) based approaches. We propose a model-informed reinforcement learning framework anchored to the analytical Angular Momentum Linear Inverted Pendulum (ALIP) template. We provide a step-to-step safety certificate for ALIP stepping via a discrete exponential control barrier function (DECBF) and use it as (i) a training-time shaping signal and (ii) a runtime action filter that minimally adjusts swing-foot placement to satisfy template-level constraints. Full-order safety is evaluated empirically on the Digit humanoid in MuJoCo with a whole-body controller stack. Compared to an unconstrained baseline, our approach reduces safety-violation events in the reported external-disturbance trial, while larger lateral-velocity transients reveal a safety-tracking tradeoff.

Robotics (cs.RO)

arXiv:2609.34486 [cs.RO]

(or arXiv:2609.34486v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.34486 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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