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J Neurophysiol 90: 3270-3282, 2003; doi:10.1152/jn.01112.2002
0022-3077/03 $5.00
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Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model

David W. Franklin1,2, Rieko Osu1, Etienne Burdet3, Mitsuo Kawato1 and Theodore E. Milner2

1ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan; 2School of Kinesiology, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada; and 3Department of Mechanical Engineering and Division of Bioengineering, National University of Singapore, 119260, Singapore

Submitted 11 December 2002; accepted in final form 16 February 2002

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Address for reprint requests: D. W. Franklin, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan (E-mail: dfrank{at}atr.co.jp).




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