MorAL: Learning Morphologically Adaptive Locomotion Controller for Quadrupedal Robots on Challenging Terrains

Abstract

Due to the rapid development of the quadruped robot industry in the past decade, various commercial quadruped robots have emerged with distinct physical attributes. Different from the previous work in which the designed controller is robot-specific, this article proposes a learning-based control framework – MorAL, which is adaptive to different morphologies of quadruped robots and challenging terrains. Our framework concurrently trains the control policy and an adaptive module, which considers the temporal robot states. This module empowers the control policy to implicitly online identify different robot platforms' properties and estimate body velocity. Extensive experiments in the real world and simulation demonstrate that our controller enables robots with significantly different morphology to overcome various indoor and outdoor harsh terrains.