Learning footstep prediction from motion capture

Abstract

Central pattern generated walking for bipedal robots has proven to be a versatile and easily implementable solution that is used by several robot soccer teams in the RoboCup Humanoid Soccer League with great success. However, the forward character of generating motor commands from an abstract, rhythmical pattern does not inherently provide the means for controlling the precise location of footsteps. For implementing a footstep planning gait control, we developed a step prediction model that estimates the location of the next footstep in Cartesian coordinates given the same inputs that control the central pattern generator. We used motion capture data recorded from walking robots to estimate the parameters of the prediction model and to verify the accuracy of the predicted footstep locations. We achieved a precision with a mean error of 1.3cm. © 2011 Springer-Verlag.