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Title: Robust Constraint-consistent Learning
Authors: Howard, Matthew
Klanke, Stefan
Gienger, Michael
Goerick, Christian
Vijayakumar, Sethu
Issue Date: 2009
Journal Title: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '09)
Abstract: 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.
Keywords: Informatics
Robotics
URI: http://hdl.handle.net/1842/3655
ISSN: 978-1-4244-3803-7
Appears in Collections:Informatics Publications

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