Bayesian Gait Optimization for Bipedal LocomotionRoberto Calandra; Nakul Gopalan; André Seyfarth; Jan Peters; Marc Peter Deisenroth
In: Panos M. Pardalos; Mauricio G. C. Resende; Chrysafis Vogiatzis; Jose L. Walteros (Hrsg.). Learning and Intelligent Optimization - 8th International Conference. Learning and Intelligent Optimization (LION-8), February 16-21, Gainesville, FL, USA, Pages 274-290, Lecture Notes in Computer Science (LNCS), Vol. 8426, Springer, 2014.
One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization.