Efficient learning of robust multigait quadruped locomotion for minimizing the cost of transport

Abstract

Quadruped robots are able to exhibit a range of gaits, each with its own traversability and energy efficiency characteristics. By actively coordinating between gaits in different scenarios, energy-efficient and adaptive locomotion can be achieved. This study investigates the performances of learned energy-efficient policies for quadrupedal gaits under different commands. We propose a training–synthesizing framework that integrates learned gait-conditioned locomotion policies into an efficient multiskill locomotion policy. The resulting control policy achieves low-cost smooth switching and controllable gaits. Our results of the learned multiskill policy demonstrate seamless gait transitions while maintaining energy optimality across all commands.