Using Hidden Markov Models to Generate Natural Humanoid Movement

摘要

This paper proposes a hidden Markov model (HMM) based approach to generate human-like movements for humanoid robots. Given human motion capture data for a class of movements, principal components are extracted for each class, and used as basis elements that in turn represent more general movements within each class. A HMM is also designed and trained for each movement class using the movement data. Humanoid movement is then generated by selecting the linear combination of basis elements that yields the highest probability for the trained HMM, subject to user-specified movement boundary conditions. The feasibility of our proposed method is demonstrated via case studies of various arm motions