A Novel Method for Learning Policies from Constrained Motion

Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference paper


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 come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. We demonstrate our approach on systems of varying complexity, including kinematic data from the ASIMO humanoid robot with 27 degrees of freedom.
Original languageEnglish
Title of host publicationICRA '09. IEEE International Conference on Robotics and Automation, 2009
Number of pages7
ISBN (Print)978-1-4244-2788-8
Publication statusPublished - 2009


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