Active online learning of the bipedal walking

For legged robot walking pattern learning, the current mainstream and state-of-the-art researches are most under a so-called computer simulation based framework, where the walking pattern is learned via a pre-established simulation platform. However, when the learned walking pattern is applied to a real robot, an additional adapting procedure is always required, due to the big difference between simulation and real walking circumstances. This turns out to be more critical for a bipedal walking, because its controlling is more difficult than others, such as quadruped robot. In this paper, a novel framework for active online learning bipedal walking directly on a physical robot is proposed. To let the learning procedure to be of both fast convergence and high efficiency, a polynomial response surrogate model, an orthogonal experimental design based active learning strategy as well as a gradient ascent algorithm are used. The experimental results on a real humanoid robot PKU-HR3 show its effectiveness, indicating that the proposed learning framework is a promising alternative for bipedal walking pattern learning.