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

Content Provider	IEEE Xplore Digital Library
Author	Lee, D. Nakamura, Y.
Copyright Year	2006
Description	Author affiliation: Dept. of Mechano-Informatics, Tokyo Univ. (Lee, D.; Nakamura, Y.)
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
Starting Page	4994
Ending Page	5000
File Size	356860
Page Count	7
File Format	PDF
ISBN	1424402581
DOI	10.1109/IROS.2006.282525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Hidden Markov models Viterbi algorithm Humanoid robots Terminology Humans Robot kinematics Educational robots Intelligent robots Stochastic systems Viterbi training mimesis motion primitive hidden Markov model
Content Type	Text
Resource Type	Article

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