PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust Constraint-consistent Learning
Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick and Sethu Vijayakumar
In: IROS 2009, 11-15 Oct 2009, St. Louis, USA.


Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations are recorded under different constraint settings. Our approach seamlessly integrates unconstrained and constrained observations by performing hybrid optimisation of two risk functionals. The first is a novel risk functional that makes a meaningful comparison between the estimated policy and constrained observations. The second is the standard risk, used to reduce the expected error under impoverished sets of constraints. We demonstrate our approach on systems of varying complexity, and illustrate its utility for transfer learning of a car washing task from human motion capture data.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5898
Deposited By:Stefan Klanke
Deposited On:08 March 2010