Stochastic Model of Imitating a New Observed Motion Based on the Acquired Motion Primitives

Abstract

Generally, imitation of a motion means generation of a close motion to the observation. Moreover, it means that conversion into its own motion, which is adoptable to its body structure, by integrating with its prior knowledge. From this perspective, a new imitation scheme is proposed. The scheme is based on hidden Markov models by employing Viterbi algorithm. The proposed scheme enables to imitate a new observed motion without learning the motion by applying its prior knowledge. Online motion primitive acquisition method is considered. Evaluation factors, such as inheritance coordinate and matching error, are introduced to evaluate imitation performance. The feasibility of the proposed scheme is demonstrated by simulation on a 20 degrees of freedom humanoid robot configuration with the evaluation factors