エネルギー消費の最小化が脚式ロボットの歩容を創発させる
Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots
機械的エネルギー消費を最小化するように学習させることで、実機の四足ロボットに速度に応じた自然な歩容（整地では馬や羊に似た構造的歩容、不整地では非構造的歩容）が創発することをシミュレーションと実機で示した論文。
著者: Zipeng Fu, Ashish Kumar, Jitendra Malik, Deepak Pathak
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged robots for efficient locomotion at different speeds. However, fixing a set of pre-programmed gaits limits the generality of locomotion. Recent animal motor studies show that these conventional gaits are only prevalent in ideal flat terrain conditions while real-world locomotion is unstructured and more like bouts of intermittent steps. What principles could lead to both structured and unstructured patterns across mammals and how to synthesize them in robots? In this work, we take an analysis-by-synthesis approach and learn to move by minimizing mechanical energy. We demonstrate that learning to minimize energy consumption plays a key role in the emergence of natural locomotion gaits at different speeds in real quadruped robots. The emergent gaits are structured in ideal terrains and look similar to that of horses and sheep. The same approach leads to unstructured gaits in rough terrains which is consistent with the findings in animal motor control. We validate our hypothesis in both simulation and real hardware across natural terrains. Videos at https://energy-locomotion.github.io
