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Guest editorial: Special issue on robot learning, Part A

Guest editorial: Special issue on robot learning, Part A Auton Robot (2009) 27: 1–2 DOI 10.1007/s10514-009-9122-2 Jan Peters · Andrew Y. Ng Received: 7 May 2009 / Accepted: 19 May 2009 / Published online: 28 May 2009 © Springer Science+Business Media, LLC 2009 1 Introduction Driven by high-profile competitions such as RoboCup and the DARPA LAGR & Learning Locomotion challenges, Creating autonomous robots that can assist humans in un- as well as the growing number of robot learning research predictable situations of daily life has been a long stand- programs funded by governments around the world, robot ing vision of robotics, artificial intelligence, and the cog- learning has become a central research problem in many nitive sciences. With the current rise of physical humanoid labs. While the interest in machine learning and statistics and other highly mechanically capable robots in robotics re- within robotics has increased substantially, robot applica- search labs around the globe, we have come a step closer to tions have become important for motivating new algorithms this aim. Thus, it has become essential to create robot sys- and formalisms in the machine learning community. As a tems that learn to accomplish a multitude of different tasks, result the interest in robot learning has reached an http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Guest editorial: Special issue on robot learning, Part A

Peters, Jan; Ng, Andrew
Autonomous Robots , Volume 27 (1) – May 28, 2009
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Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Computer Science; Simulation and Modeling; Mechanical Engineering; Computer Imaging, Vision, Pattern Recognition and Graphics; Electrical Engineering; Control , Robotics, Mechatronics; Artificial Intelligence (incl. Robotics)
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-009-9122-2
Publisher site
See Article on Publisher Site

Abstract

Auton Robot (2009) 27: 1–2 DOI 10.1007/s10514-009-9122-2 Jan Peters · Andrew Y. Ng Received: 7 May 2009 / Accepted: 19 May 2009 / Published online: 28 May 2009 © Springer Science+Business Media, LLC 2009 1 Introduction Driven by high-profile competitions such as RoboCup and the DARPA LAGR & Learning Locomotion challenges, Creating autonomous robots that can assist humans in un- as well as the growing number of robot learning research predictable situations of daily life has been a long stand- programs funded by governments around the world, robot ing vision of robotics, artificial intelligence, and the cog- learning has become a central research problem in many nitive sciences. With the current rise of physical humanoid labs. While the interest in machine learning and statistics and other highly mechanically capable robots in robotics re- within robotics has increased substantially, robot applica- search labs around the globe, we have come a step closer to tions have become important for motivating new algorithms this aim. Thus, it has become essential to create robot sys- and formalisms in the machine learning community. As a tems that learn to accomplish a multitude of different tasks, result the interest in robot learning has reached an

Journal

Autonomous Robots – Springer Journals

Published: May 28, 2009

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