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ICRA
2009
IEEE

A novel method for learning policies from constrained motion

9 years 1 months ago
A novel method for learning policies from constrained motion
— 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.
Matthew Howard, Stefan Klanke, Michael Gienger, Ch
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICRA
Authors Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar
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