A Novel Method for Learning Policies from Constrained Motion

TY - CHAP

T1 - A Novel Method for Learning Policies from Constrained Motion

AU - Howard, Matthew

AU - Klanke, Stefan

AU - Gienger, Michael

AU - Goerick, Christian

AU - Vijayakumar, Sethu

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

U2 - 10.1109/ROBOT.2009.5152335

DO - 10.1109/ROBOT.2009.5152335

M3 - Conference paper

SN - 978-1-4244-2788-8

SP - 1717

EP - 1723

BT - ICRA '09. IEEE International Conference on Robotics and Automation, 2009

PB - IEEE

ER -