Gradient-driven online learning of bipedal push recovery

Abstract

Bipedal walking is a complex and dynamic whole-body motion with balance constraints. Due to the inherently unstable inverted pendulum-like dynamics of walking, the design of robust walking controllers proved to be particularly challenging. While a controller could potentially be learned with a robot in the loop, the destructive nature of losing balance and the impracticality of a high number of repetitions render most existing learning methods unsuitable for an online learning setting with real hardware. We propose a model-driven learning method that enables a humanoid robot to quickly learn how to maintain its balance. We bootstrap the learning process with a central pattern generator for stepping motions that abstracts from the complexity of the walking motion and simplifies the problem setting to the learning of a small number of leg swing amplitude parameters. A simple physical model that represents the dominant dynamics of bipedal walking estimates an approximate gradient and suggests how to modify the swing amplitude to restore balance. In experiments with a real robot, we show that only a few failed steps are sufficient for our biped to learn strong push recovery skills in the sagittal direction.