Pseudo Direct and Inverse Optimal Control based on Motion Synthesis using FPCA

Abstract

This paper presents a method to estimate cost weights of cost functions and multiple joint motion time-series values of humanoid robots easily, using functional principal component analysis (FPCA) instead of direct optimal control (DOC) and inverse optimal control (IOC). Each given object’s cost weight exemplar can be converted into a point in the FPC space by applying FPCA. Cost weight and the FPC space enable to synthesize the motion model data and the cost function factor and therefore versatile motion data conveniently. The proposed method surpasses classic DOC and IOC methods in terms of calculation time and efficiency, in novel data analysis. Furthermore, proposed method is applied to the humanoid robot HRP4, to generate arm motions, as an experimental proof of concept with some cost functions. The accuracy of the motion generation is experimentally confirmed.