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TITLE: Equilibrium Points: A unifying abstraction for motor control
PRESENTER: Professor Dana H. Ballard, http://www.cs.rochester.edu/~dana, dana@cs.rochester.edu
AFFILIATION:Computer Science, Brain & Cognitive Sciences, University of Rochester, http://www.cs.rochester.edu
DATE: Tuesday 9th May 2006
TIME: 12:00:00
PLACE: CSE Seminar Room, Level 1, K17
ABSTRACT:
In the human musculoskeletal system, a huge amount of energy is stored
passively in the biomechanics of the muscle system. Controlling such a
system in a way that takes advantage of the stored energy has lead to the
Equilibrium-point hypothesis (EPH). This hypothesis states that the Central
Nervous System computes the equilibrium points (EPs) for a task and
movements are achieved by passively attracting the muscular system to those
EPs. Although forty years have passed since the EPH was initially proposed
by Feldman, little research has been directed to how those Equilibrium
points are calculated. We propose a motor simulation model to compute the
EPs during the motor planning phase. In motor simulation, gradient descent
is used to gradually steer the end effector to the target to get one
inverse kinematics solution. Since this solution might not be
nergy-efficient, another simulation with the end effector pinned is used
to change the joint configuration. In movement execution, damped springs
are simulated as the abstraction of actual muscles. Given the EPs planned
in the first phase, spring natural lengths are configured and movements
are generated as far as the spring actual lengths are deviated from the
end-points. The model is first verified in a two-dimensional reaching
task, and further extended to control more complex movements, such as
walking and sitting down where predictions and find that our model is
general and powerful enough to depict both simple and complex movements.
BIOGRAPHY OF SPEAKER:
Professor Dana H. Ballard's main research interest is in computational
theories of the brain with emphasis on human vision. In 1985 Professor
Ballard and Chris Brown led a team that designed and built a high speed
binocular camera control system capable of simulating human eye movements.
The system was mounted on a robotic arm that allowed it to move at one
meter per second in a two meter radius workspace. This system has led
to an increased understanding of the role of behavior in vision.
The theoretical aspects of that system were summarized in a paper
``Animate Vision,'' which received the Best Paper Award at the 1989
International Joint Conference on Artificial Intelligence.
Currently he is interested in pursuing this research by using model
humans in virtual reality environments. In addition he is interested
in models of the brain that relate to detailed neural codes. A position
paper on this work appeared in the Behavioral and Brain Sciences.
Host:
Bernhard Hengst and Claude Sammut
Seminar Convenor:
Van Hai Ho
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