Robot learning [TC Spotlight]
Author(s)Tedrake, Russell Louis; Roy, Nicholas; Peters, Jan; Morimoto, Jun
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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.
DepartmentMassachusetts 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
IEEE Robotics & Automation Magazine
Institute of Electrical and Electronics Engineers
Peters, J. et al. “Robot learning [TC Spotlight].” Robotics & Automation Magazine, IEEE 16.3 (2009): 19-20. © 2009 IEEE.
Final published version
INSPEC Accession Number: 10864241