Supervised learning for stabilizing underactuated bipedal robot locomotion, with outdoor experiments on the wave field

Abstract

Supervised learning is used to build a control policy for robust, stable, dynamic walking of an underactuated bipedal robot. The training and testing sets consist of controllers based on a full dynamic model, virtual constraints, and parameter optimization to meet torque limits, friction cone, and environmental conditions. The controllers are designed to induce locally exponentially stable periodic walking gaits at various speeds, both forward and backward, and for various constant ground slopes. They are also designed to induce aperiodic gaits that transition among a subset of the periodic gaits in a fixed number of steps. In experiments, the learned policy allows a 3D bipedal robot to recover from a significant kick. It also enables the robot to walk down a 22 degree slope and walk on sinusoidally varying terrain, all without using a camera. During the development of these results, it is demonstrated that supervised learning of locally exponentially stable controllers can result in a loss of stability and a means to avoid this is suggested.