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JRM Vol.34 No.4 pp. 786-794
doi: 10.20965/jrm.2022.p0786
(2022)

Paper:

Grid-Based Estimation of Transformation Between Partial Relationships Using a Genetic Algorithm

Sota Nakamura, Yuichi Kobayashi, and Taisei Matsuura

Shizuoka University
3-5-1 Johoku, Naka-ku, Hamamatsu 432-8561, Japan

Received:
March 2, 2022
Accepted:
June 2, 2022
Published:
August 20, 2022
Keywords:
human motor learning, estimation of transformation, hyper adaptability
Abstract
Grid-Based Estimation of Transformation Between Partial Relationships Using a Genetic Algorithm

Transformation estimation to discover the reflection relation

Human motor learning is characterized by adaptation, wherein information obtained in the past is transferred to a different situation. In this study, we investigate a grid-based computation for explaining the reuse of the information of an existing controller for adaptation to a partial malfunction of a controller. To this end, a motor learning scheme is adopted based on the detection and estimation of partial relationships. The transformation between the partial relationships is estimated based on a grid-based estimation of the two coordinate systems. In this estimation, the coordinate systems are optimized using a genetic algorithm. Two arms in a reflection are considered, and it is confirmed that the transformation of the differential kinematics (Jacobian), as an example of the partial relationships, can be estimated by the proposed method.

Cite this article as:
S. Nakamura, Y. Kobayashi, and T. Matsuura, “Grid-Based Estimation of Transformation Between Partial Relationships Using a Genetic Algorithm,” J. Robot. Mechatron., Vol.34, No.4, pp. 786-794, 2022.
Data files:
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Last updated on Sep. 22, 2022