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


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

March 2, 2022
June 2, 2022
August 20, 2022
human motor learning, estimation of transformation, hyper adaptability

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.

Transformation estimation to discover the reflection relation

Transformation estimation to discover the reflection relation

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.
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