Howard, Matthew

Klanke, Stefan

Gienger, Michael

Goerick, Christian

Vijayakumar, Sethu

Sigaud, Olivier

Peters, Jan

2010-08-18T15:45:15Z

2010-08-18T15:45:15Z

2010

2010-08-18T15:45:15Z

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.

39

10.1007/978-3-642-05181-4_12

1860949X

http://www.springerlink.com/content/u17266g284427534/

http://hdl.handle.net/1842/3650

en

Springer-Verlag

Informatics

Computer Science

Methods for Learning Control Policies from Variable Constraint Demonstrations

Book Chapter

From Motor Learning to Interaction Learning in Robots