Georgios Petkos

Sethu Vijayakumar

2010-08-31T13:44:43Z

2010-08-31T13:44:43Z

2007-04

2010-08-31T13:44:43Z

Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it's use for robust predictive control. However, in realistic domains, system dynamics often change based on unobserved external contexts such as work load or contact conditions with other objects. Previous multiple model approaches to solving this problem are restricted to finite, discrete contexts without any generalization and have been tested only on linear systems. We present a framework for estimation of context through hidden latent variable extraction -- solely from experienced (non-linear) dynamics. This work refines the multiple model formalism to bootstrap context separation from context-unlabeled data and enables simultaneous online context estimation, dynamics learning and control based on a consistent probabilistic formulation. Most importantly, it extends the framework to a continuous latent model representation of context under specific assumptions of load distribution.

2117-2123

10.1109/ROBOT.2007.363634

1-4244-0601-3

0983

http://hdl.handle.net/1842/3696

IEEE

Informatics Report Series

EDI-INF-RR-0983

Learning control

Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts

Conference Paper

IEEE International Conference on Robotics and Automation (ICRA '07)