VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation

Abstract

Quadruped locomotion is rapidly maturing to a degree where robots are able to realize highly dynamic maneuvers. However, current planners are unable to vary key gait parameters of the in-swing feet midair . In this article, we address this limitation and show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. This is achieved via a generative model trained on a single trot style, which encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesizing a continuous variety of trot styles. We demonstrate that specific properties of the drive signal map directly to gait parameters, such as cadence, footstep height, and full-stance duration. Due to the nature of our approach, these synthesized gaits are continuously variable online during robot operation. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework. We evaluate our approach on two versions of the real ANYmal quadruped robots and demonstrate that our method achieves a continuous blend of dynamic trot styles while being robust and reactive to external perturbations.