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