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Abstract:
Tangential hand velocity profiles of rapid human arm movements
often appear as sequences of several bell-shaped
acceleration-deceleration phases called submovements or movement
units. This suggests how the nervous system might efficiently
control a motor plant in the presence of noise and feedback
delay. Another critical observation is that stochasticity in a
motor control problem makes the optimal control policy
essentially different from the optimal control policy for the
deterministic case. We use a simplified dynamic model of an arm
and address rapid aimed arm movements. We use reinforcement
learning as a tool to approximate the optimal policy in the
presence of noise and feedback delay. Using a simplified model we
show that multiple submovements emerge as an optimal policy in
the presence of noise and feedback delay. The optimal policy in
this situation is to drive the arm's end point close to the
target by one fast submovement and then apply a few slow
submovements to accurately drive the arm's end point into the
target region. In our simulations, the controller sometimes
generates corrective submovements before the initial fast
submovement is completed, much like the predictive corrections
observed in a number of psychophysical experiments.
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