| |
Abstract:
Humans demonstrate a remarkable ability to generate accurate
and appropriate motor behavior under many different and often
uncertain environmental conditions. This paper describes a new
modular approach to human motor learning and control, based on
multiple pairs of inverse (controller) and forward (predictor)
models. This architecture simultaneously learns the multiple
inverse models necessary for control as well as how to select the
inverse models appropriate for a given environment. Simulations of
object manipulation demonstrates the ability to learn multiple
objects, appropriate generalization to novel objects and the
inappropriate activation of motor programs based on visual cues,
followed by on-line correction, seen in the "size-weight
illusion".
|
