The RACE Project: Robustness by Autonomous Competence Enhancement

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions...

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Bibliographic Details
Published in:KI. Künstliche Intelligenz (Oldenbourg) 2014-11, Vol.28 (4), p.297-304
Main Authors: Hertzberg, Joachim, Zhang, Jianwei, Zhang, Liwei, Rockel, Sebastian, Neumann, Bernd, Lehmann, Jos, Dubba, Krishna S. R., Cohn, Anthony G., Saffiotti, Alessandro, Pecora, Federico, Mansouri, Masoumeh, Konečný, Štefan, Günther, Martin, Stock, Sebastian, Lopes, Luis Seabra, Oliveira, Miguel, Lim, Gi Hyun, Kasaei, Hamidreza, Mokhtari, Vahid, Hotz, Lothar, Bohlken, Wilfried
Format: Magazinearticle
Language:English
Subjects:
Artificial Intelligence
Computer Science
Datavetenskap
Research Project
Software Engineering/Programming and Operating Systems
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Summary:This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.
ISSN:0933-1875
1610-1987
1610-1987
DOI:10.1007/s13218-014-0327-y