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Imitation Learning of Dual-Arm Manipulation Tasks in Humanoid Robots

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4 Author(s)
Asfour, T. ; Inst. for Comput. Sci. & Eng., Karlsruhe Univ. ; Gyarfas, F. ; Azad, P. ; Dillmann, R.

In this paper, we deal with imitation learning of arm movements in humanoid robots. Hidden Markov models (HMM) are used to generalize movements demonstrated to a robot multiple times. They are trained with the characteristic features (key points) of each demonstration. Using the same HMM, key points that are common to all demonstrations are identified; only those are considered when reproducing a movement. We also show how HMM can be used to detect temporal dependencies between both arms in dual-arm tasks. We created a model of the human upper body to simulate the reproduction of dual-arm movements and generate natural-looking joint configurations from tracked hand paths. Results are presented and discussed

Published in:

Humanoid Robots, 2006 6th IEEE-RAS International Conference on

Date of Conference:

4-6 Dec. 2006

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