Adaptation of Motor Control Through Transferring Mirror-Image Kinematics Between Dual Arms

Abstract

Developing a learning model that adapts to changes in the body is critical for improving the flexibility of machine intelligence. During recovery from a controller malfunction, humans use the information obtained from previous experiences. One possible explanation for the recovery process is that information from the remaining controller was transformed and used. Modeling this mechanism will aid in the development of an adaptive motor-learning model capable of quickly recovering from controller malfunctions. We proposed a learning model for explaining the reused information of the remaining controllers in a pair of controllers. Simulations of a pair of upper limbs validated that the learning model could find a simple transformation, such as a reflection between the left and right arms, using optimization.