Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
ICANN 2006 proceedings
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Date
09/2006Author
Petkos, Georgios
Toussaint, Marc
Vijayakumar, Sethu
Metadata
Abstract
For stationary systems, efficient techniques for adaptive motor control
exist which learn the system’s inverse dynamics online and use this single model
for control. However, in realistic domains the system dynamics often change depending
on an external unobserved context, for instance the work load of the
system or contact conditions with other objects. A solution to context-dependent
control is to learn multiple inverse models for different contexts and to infer the
current context by analyzing the experienced dynamics. Previous multiple model
approaches have only been tested on linear systems. This paper presents an efficient
multiple model approach for non-linear dynamics, which can bootstrap
context separation from context-unlabeled data and realizes simultaneous online
context estimation, control, and training ofmultiple inverse models. The approach
formulates a consistent probabilistic model used to infer the unobserved context
and uses Locally Weighted Projection Regression as an efficient online regressor
which provides local confidence bounds estimates used for inference.