Reproduce “Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains” on OpenResearch
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About this paper
Ammar Issa, Anubhav Singh, Anton Tsaritsin, Sergey Kolyubin
The authors present a hierarchical learned controller that adapts quadruped gait. A slower high-level policy adjusts gait parameters, while a faster low-level policy converts them into motion. Evaluation shows pacing, walking, then trotting as speed rises. Go1 simulation reports 27.7% lower average CoT than AER, but not at every speed; hardware evidence covers limited trials.
Summary by alphaXiv.
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