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Learning Dance Movements by Imitation: A Multiple Model Approach

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KI 2008: Advances in Artificial Intelligence (KI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5243))

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Abstract

Imitation learning is an intuitive and easy way of programming robots. Instead of specifying motor commands, you simply show the robot what to do. This paper presents a modular connectionist architecture that enables imitation learning in a simulated robot. The robot imitates human dance movements, and the architecture self-organizes the decomposition of movements into submovements, which are controlled by different modules. Modules both dominate and collaborate during control of the robot. Low-level examination of the inverse models (i.e. motor controllers) reveals a recurring pattern of neural activity during repetition of movements, indicating that the modules successfully capture specific parts of the trajectory to be imitated.

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Authors and Affiliations

  1. IDI, Norwegian University of Science and Technology,  

    Axel Tidemann & Pinar Öztürk

Authors
  1. Axel Tidemann
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  2. Pinar Öztürk
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Editor information

Andreas R. Dengel Karsten Berns Thomas M. Breuel Frank Bomarius Thomas R. Roth-Berghofer

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Tidemann, A., Öztürk, P. (2008). Learning Dance Movements by Imitation: A Multiple Model Approach. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds) KI 2008: Advances in Artificial Intelligence. KI 2008. Lecture Notes in Computer Science(), vol 5243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85845-4_47

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  • DOI: https://doi.org/10.1007/978-3-540-85845-4_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85844-7

  • Online ISBN: 978-3-540-85845-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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