A Kendama learning robot based on bi-directional theory

Hiroyuki Miyamoto, Stefan Schaal, Francesca Gandolfo, Hiroaki Gomi, Yasuharu Koike, Rieko Osu, Eri Nakano, Yasuhiro Wada, Mitsuo Kawato

Research output: Contribution to journalArticle

98 Citations (Scopus)

Abstract

A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions.

Original languageEnglish
Pages (from-to)1281-1302
Number of pages22
JournalNeural Networks
Volume9
Issue number8
DOIs
Publication statusPublished - 1996 Nov
Externally publishedYes

Keywords

  • dynamic optimization
  • task-level learning
  • teaching by showing

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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  • Cite this

    Miyamoto, H., Schaal, S., Gandolfo, F., Gomi, H., Koike, Y., Osu, R., Nakano, E., Wada, Y., & Kawato, M. (1996). A Kendama learning robot based on bi-directional theory. Neural Networks, 9(8), 1281-1302. https://doi.org/10.1016/S0893-6080(96)00043-3