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Cited by 3,474

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Bettering operation of Robots by learning

Arimoto, Suguru, Kawamura, Sadao, Miyazaki, Fumio·1984·Journal of Robotic Systems

Abstract

This article proposes a betterment process for the operation of a mechanical robot in a sense that it betters the next operation of a robot by using the previous operation's data. The process has an iterative learning structure such that the (k + 1)th input to joint actuators consists of the k th input plus an error increment composed of the derivative difference between the k th motion trajectory and the given desired motion trajectory. The convergence of the process to the desired motion trajectory is assured under some reasonable conditions. Numerical results by computer simulation are presented to show the effectiveness of the proposed learning scheme.

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Metadata

Authors
Arimoto, S., Kawamura, S., & Miyazaki, F.
Venue
Journal of Robotic Systems
Publisher
Wiley
Published
1984-06
Volume
1
Issue
2
Pages
123-140
ISSN
  • 0741-2223 (ISSN-L)
  • 1097-4563
OpenAlex
W2146396918
Cited by
3,474
References
1
FWCI
29.62
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bettering operation of Robots by learning. https://app.cochat.ai/papers/10.1002/rob.4620010203
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Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of Robots by learning. Journal of Robotic Systems. https://doi.org/10.1002/rob.4620010203

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.