Natural Movement Generation Using Hidden Markov Models and Principal Components

Abstract

Recent studies have shown that the perception of natural movements-in the sense of being "humanlike"-depends on both joint and task space characteristics of the movement. This paper proposes a movement generation framework that merges two established techniques from gesture recognition and motion generation-hidden Markov models (HMMs) and principal components-into an efficient and reliable means of generating natural movements, which uniformly considers joint and task space characteristics. Given human motion data that are classified into several movement categories, for each category, the principal components extracted from the joint trajectories are used as basis elements. An HMM is, in turn, designed and trained for each movement class using the human task space motion data. Natural movements are generated as the optimal linear combination of principal components, which yields the highest probability for the trained HMM. Experimental case studies with a prototype humanoid robot demonstrate the various advantages of our proposed framework.