ICRA'09 Paper Abstract


Paper FrA6.1

Howard, Matthew (University of Edinburgh), Klanke, Stefan (University of Edinburgh), Gienger, Michael (Honda Research Institute Europe), Goerick, Christian (Honda Reserach Inst. Europe GmbH), Vijayakumar, Sethu (University of Edinburgh)

A Novel Method for Learning Policies from Constrained Motion

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - I" (FrA6), Friday, May 15, 2009, 08:30−08:50, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 21, 2022

Keywords Learning and Adaptive Systems, Humanoid Robots, Kinematics


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 are frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control polices 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.



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