Feed-Forward Adaptation to a Varying Dynamic Environment During Reaching Movements
Author(s) -
Koji Ito,
Makoto Doi,
Toshiyuki Kondo
Publication year - 2007
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2007.p0474
Subject(s) - adaptation (eye) , control theory (sociology) , movement (music) , representation (politics) , force field (fiction) , position (finance) , computer science , internal model , field (mathematics) , feed forward , control engineering , control (management) , physics , engineering , mathematics , artificial intelligence , neuroscience , psychology , acoustics , finance , politics , political science , pure mathematics , law , economics
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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