PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Methods for Learning Control Policies from Variable Constraint Demonstrations
Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick and Sethu Vijayakumar
In: From Motor Learning to Interaction Learning in Robots Studies in Computational Intelligence , 264 . (2010) Springer Verlag , pp. 253-291. ISBN 978-3-642-05180-7


Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually not observable and frequently change between contexts. In this chapter, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints onmotion.We discuss how an effective approach for doing this is to learn the unconstrained policy in a way that is consistent with the constraints.We then go on to discuss several recent algorithms for extracting policies from movement data, where observations are recorded under variable, unknown constraints. We review a number of experiments testing the performance of these algorithms and demonstrating how the resultant policymodels generalise over constraints allowing prediction of behaviour under unseen settings where new constraints apply

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5900
Deposited By:Stefan Klanke
Deposited On:08 March 2010