Methods for Learning Control Policies from Variable-Constraint Demonstrations

  • Matthew Howard
  • Stefan Klanke
  • Michael Gienger
  • Christian Goerick
  • Sethu Vijayakumar
Chapter

Abstract

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 on motion. 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 policy models generalise over constraints allowing prediction of behaviour under unseen settings where new constraints apply.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matthew Howard
    • 1
  • Stefan Klanke
    • 1
  • Michael Gienger
    • 2
  • Christian Goerick
    • 2
  • Sethu Vijayakumar
    • 1
  1. 1.Institute of Perception Action and BehaviourUniversity of EdinburghScotlandUK
  2. 2.Honda Research Institute Europe (GmBH)OffenbachGermany

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