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Legged locomotion is commonly studied and expressed as a discrete set of gaitpatterns, like walk, trot, gallop, which are usually treated as given andpre-programmed in legged robots for efficient locomotion at different speeds.However, fixing a set of pre-programmed gaits limits the generality oflocomotion. Recent animal motor studies show that these conventional gaits areonly prevalent in ideal flat terrain conditions while real-world locomotion isunstructured and more like bouts of intermittent steps. What principles couldlead to both structured and unstructured patterns across mammals and how tosynthesize them in robots? In this work, we take an analysis-by-synthesisapproach and learn to move by minimizing mechanical energy. We demonstrate thatlearning to minimize energy consumption plays a key role in the emergence ofnatural locomotion gaits at different speeds in real quadruped robots. Theemergent gaits are structured in ideal terrains and look similar to that ofhorses and sheep. The same approach leads to unstructured gaits in roughterrains which is consistent with the findings in animal motor control. Wevalidate our hypothesis in both simulation and real hardware across naturalterrains. Videos at https://energy-locomotion.github.io

Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots