Robot learning [TC Spotlight]
Name
Peters-2009-Robot learning [TC Spotlight].pdf
Size
725.3 KB
Format
Adobe PDF
Checksum (MD5)
f481552c8b78321caccc356526ef0728
Author(s)
Tedrake, Russell Louis
Roy, Nicholas
Peters, Jan
Morimoto, Jun
Date Issued
September 2009
Journal
IEEE Robotics & Automation Magazine
Publisher
Institute of Electrical and Electronics Engineers
Citation
Peters, J. et al. “Robot learning [TC Spotlight].” Robotics & Automation Magazine, IEEE 16.3 (2009): 19-20. © 2009 IEEE.
Version
Final published version
Abstract
Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/MRA.2009.933618
