Grid-Based Estimation of Transformation Between Partial Relationships Using a Genetic Algorithm
Sota Nakamura, Yuichi Kobayashi, and Taisei Matsuura
3-5-1 Johoku, Naka-ku, Hamamatsu 432-8561, Japan
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.
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