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
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
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