Feed-Forward Adaptation to a Varying Dynamic Environment During Reaching Movements
Koji Ito*, Makoto Doi**, and Toshiyuki Kondo***
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259-G3-50 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**DENSO Corporation, 1-1 Showa-cho, Kariya, Aichi 448-8661, Japan
***Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, Japan
Humans must compensate for the reaction forces arising from interaction with the physical environment. Recent studies have shown that humans can acquire a neural representation of the relationship between motor commands and movement, i.e. learn an internal model of environmental dynamics. We discuss feed-forward adaptation in a varying dynamic environment during reaching movements. Subjects first learned to move in a position-dependent divergent force field (DF) and velocity-dependent force field (VF), then move in a switched force field SF1 (DF→VF) and SF2 (VF→DF). The experimental results show that adaptation to switched force fields is achieved by programming the internal model control and impedance control in a feed-forward manner.
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