Adaptive optimal feedback control with learned internal dynamics models

D. Mitrovic, S. Klanke, S. Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Optimal Feedback Control (OFC) has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the Iterative Linear Quadratic Gaussian (ILQG) algorithm, have focused on the case of non-linear, but still analytically available, dynamics. For realistic control systems, however, the dynamics may often be unknown, difficult to estimate, or subject to frequent systematic changes. In this chapter, we combine the ILQG framework with learning the forward dynamics for simulated arms, which exhibit large redundancies, both, in kinematics and in the actuation. We demonstrate how our approach can compensate for complex dynamic perturbations in an online fashion. The specific adaptive framework introduced lends itself to a computationally more efficient implementation of the ILQG optimisation without sacrificing control accuracy – allowing the method to scale to large DoF systems.
Original languageEnglish
Title of host publicationFrom Motor Learning to Interaction Learning in Robots
PublisherSpringer-Verlag GmbH
Pages65-84
Number of pages20
ISBN (Print)978-3-642-05180-7
DOIs
Publication statusPublished - 2010

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Berlin / Heidelberg
Volume264
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Fingerprint

Dive into the research topics of 'Adaptive optimal feedback control with learned internal dynamics models'. Together they form a unique fingerprint.

Cite this