Kendama learning robot based on a dynamic optimization theory

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

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

A general theory of movement pattern perception based on a dynamic optimization theory can be used for motion capture and learning by watching in robotics. We exemplify our methods for the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has exactly the same kinematic structure as a human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients were (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.

Original languageEnglish
Pages327-332
Number of pages6
Publication statusPublished - 1995 Dec 1
Externally publishedYes
EventProceedings of the 1995 4th IEEE International Workshop on Robot and Human Communication, RO-MAN - Tokyo, Jpn
Duration: 1995 Jul 51995 Jul 7

Other

OtherProceedings of the 1995 4th IEEE International Workshop on Robot and Human Communication, RO-MAN
CityTokyo, Jpn
Period95/7/595/7/7

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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    Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Osu, R., Nakano, E., Wada, Y., & Kawato, M. (1995). Kendama learning robot based on a dynamic optimization theory. 327-332. Paper presented at Proceedings of the 1995 4th IEEE International Workshop on Robot and Human Communication, RO-MAN, Tokyo, Jpn, .