NDLI: Learning to control planar hitting motions in a minigolf-like task

Content Provider	IEEE Xplore Digital Library
Author	Kronander, K. Khansari-Zadeh, M.S.M. Billard, A.
Copyright Year	2011
Description	Author affiliation: Learning Algorithms and Systems Laboratory (LASA), Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland (Kronander, K.; Khansari-Zadeh, M.S.M.; Billard, A.)
Abstract	A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a nontrivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment.
Starting Page	710
Ending Page	717
File Size	356664
Page Count	8
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6094402
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Training Vectors Ground penetrating radar Training data Trajectory Sports equipment
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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