Whole-Body Motion Blending Under Physical Constraints Using Functional PCA

Abstract

This paper presents a method for motion synthesis using Functional Principal Component Analysis (Functional PCA) to generate complex humanoid robot motions in a low-dimensional space while considering physical consistency. Since each motion can be expressed by a point in a space called FPC space, this method allows blending different motions. For more complex motion synthesis, we introduce a novel framework to synthesize blended motions by configuring a local FPC space and a global FPC space. This method enables to merge data while considering data features. However, physical consistency was not ensured in our previous work, we here apply optimization under constraints after synthesis. We show the dynamic feasibility and the feature of the synthesized blended motions and also an interesting observation opening to the possibility to generate a variety of motions from a few motion data in a local space at low cost and time.