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 this https URL

本文利用机器人的实验研究方法研究了四足机器人在不同地形下的学习最小化机械能消耗以实现不同速度下的自然运动步态。本文证实了这一假设和在仿真和现实硬件的自然地形中的验证，这种方法也能够实现非结构化的奔跑步态，与动物的运动控制研究相一致。