Human-Inspired Adaptive Gait Learning for Humanoids Locomotion

Abstract

Achieving natural, robust, and energy-efficient locomotion remains a central challenge for humanoid control. While imitation learning enables robots to reproduce human-like behaviors, differences in morphology, actuation, and partial observability often limit direct motion replication. This work proposes a human-inspired reinforcement learning framework that integrates both implicit and explicit guidance. Implicit human motion priors, obtained through adversarial learning, provide style alignment with human data, while explicit biomechanical rewards encode characteristic gait principles to promote symmetry, stability, and adaptability. In addition, a history-based state estimator explicitly reconstructs base velocities from partial observations, mitigating observability gaps and enhancing robustness in real-world settings. To assess human-likeness, we introduce a tri-metric evaluation protocol covering gait symmetry, human-robot similarity, and energy efficiency. Extensive experiments demonstrate that the proposed approach produces locomotion that is not only robust and transferable across diverse terrains but also energy-efficient and recognizably human-like.