A Novel Method for Learning Policies from Constrained Motion
Proc. IEEE International Conference on Robotics and Automation (ICRA '09)
Date
2009Author
Howard, Matthew
Klanke, Stefan
Gienger, Michael
Goerick, Christian
Vijayakumar, Sethu
Metadata
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 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.