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Computer Science @ Rochester
Tuesday, February 12, 2008
11:00 AM
Computer Studies Bldg. Room 703
Manu Chhabra
University of Rochester
Optimality in Motor Control
Several models of human motor control propose that stereotypical movements like reaching are optimal, in the sense that they minimize a performance cost function. However, from a computational point of view, optimal control is a hard problem. First, most natural bio-mechanical systems are redundant---there are more degrees of freedom than required. Second, optimal control tasks often suffer from the ``curse of dimensionality''. For example, consider moving a two-joint arm from location A to location B in 100 time steps. If torques are discretized to one of 10 values, then there are 10200 possible torque sequences. How do we efficiently search a space of that size? In this talk, we address these issues from the perspective of optimal control theory.

First, if optimal control is hard, how do humans accurately solve day to day control problems? In the first part of the talk, I propose that for stereotypical movements like reaching, the optimal control solution can be decomposed as a sum of scaled and time shifted components, or ``motor synergies''. These synergies were discovered through dimensionality reduction. Near-optimal control is achieved by linearly combining the synergies. Second, are humans optimal under non-stereotypical conditions? In the second part of the talk, I present results from experiments that show that humans are able to perform well even under certain non-stereotypical control regimes. In these experiments, subjects were required to control a dynamical system corrupted with noise. A comparison of human performance to the theoretically optimal solution shows that humans reach close to optimal performance under a variety of noise conditions. Additionally, we showed that subjects adapt to the noise regime rather than using a fixed controller across different noise conditions.