The RACE Project: Robustness by Autonomous Competence Enhancement

Joachim Hertzberg; Jianwei Zhang; Liwei Zhang; Sebastian Rockel; Bernd Neumann; Jos  Lehmann; Krishna S R  Dubba; Anthony G Cohn; Alessandro Saffiotti; Federico Pecora; Masoumeh Mansouri; Stefan Konecny; Martin Gunther; Sebastian Stock; Luis Seabra Lopes; Miguel Oliveira; Gi Hyun Lim; Hamidreza Kasaei; Vahid Mokhtari; Lothar Hotz; Wilfried Bohlken

doi:10.1007/s13218-014-0327-y

The RACE Project: Robustness by Autonomous Competence Enhancement

Joachim Hertzberg, Jianwei Zhang, Liwei Zhang, Sebastian Rockel, Bernd Neumann, Jos Lehmann, Krishna S R Dubba, Anthony G Cohn, Alessandro Saffiotti, Federico Pecora, Masoumeh Mansouri, Stefan Konecny, Martin Gunther, Sebastian Stock, Luis Seabra Lopes, Miguel Oliveira, Gi Hyun Lim, Hamidreza Kasaei, Vahid Mokhtari, Lothar HotzWilfried Bohlken

Machine Learning and Robotics

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	297-304
Number of pages	8
Journal	Künstliche Intelligenz
Volume	28
DOIs	https://doi.org/10.1007/s13218-014-0327-y
Publication status	Published - 2014

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Hertzberg, J., Zhang, J., Zhang, L., Rockel, S., Neumann, B., Lehmann, J., Dubba, K. S. R., Cohn, A. G., Saffiotti, A., Pecora, F., Mansouri, M., Konecny, S., Gunther, M., Stock, S., Lopes, L. S., Oliveira, M., Lim, G. H., Kasaei, H., Mokhtari, V., ... Bohlken, W. (2014). The RACE Project: Robustness by Autonomous Competence Enhancement. Künstliche Intelligenz, 28, 297-304. https://doi.org/10.1007/s13218-014-0327-y

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title = "The RACE Project: Robustness by Autonomous Competence Enhancement",

abstract = "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.",

author = "Joachim Hertzberg and Jianwei Zhang and Liwei Zhang and Sebastian Rockel and Bernd Neumann and Jos Lehmann and Dubba, {Krishna S R} and Cohn, {Anthony G} and Alessandro Saffiotti and Federico Pecora and Masoumeh Mansouri and Stefan Konecny and Martin Gunther and Sebastian Stock and Lopes, {Luis Seabra} and Miguel Oliveira and Lim, {Gi Hyun} and Hamidreza Kasaei and Vahid Mokhtari and Lothar Hotz and Wilfried Bohlken",

year = "2014",

doi = "10.1007/s13218-014-0327-y",

language = "English",

volume = "28",

pages = "297--304",

journal = "K{\"u}nstliche Intelligenz",

issn = "1610-1987",

publisher = "Springer Nature",

}

Hertzberg, J, Zhang, J, Zhang, L, Rockel, S, Neumann, B, Lehmann, J, Dubba, KSR, Cohn, AG, Saffiotti, A, Pecora, F, Mansouri, M, Konecny, S, Gunther, M, Stock, S, Lopes, LS, Oliveira, M, Lim, GH, Kasaei, H, Mokhtari, V, Hotz, L & Bohlken, W 2014, 'The RACE Project: Robustness by Autonomous Competence Enhancement', Künstliche Intelligenz, vol. 28, pp. 297-304. https://doi.org/10.1007/s13218-014-0327-y

TY - JOUR

T1 - The RACE Project

T2 - Robustness by Autonomous Competence Enhancement

AU - Hertzberg, Joachim

AU - Zhang, Jianwei

AU - Zhang, Liwei

AU - Rockel, Sebastian

AU - Neumann, Bernd

AU - Lehmann, Jos

AU - Dubba, Krishna S R

AU - Cohn, Anthony G

AU - Saffiotti, Alessandro

AU - Pecora, Federico

AU - Mansouri, Masoumeh

AU - Konecny, Stefan

AU - Gunther, Martin

AU - Stock, Sebastian

AU - Lopes, Luis Seabra

AU - Oliveira, Miguel

AU - Lim, Gi Hyun

AU - Kasaei, Hamidreza

AU - Mokhtari, Vahid

AU - Hotz, Lothar

AU - Bohlken, Wilfried

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/s13218-014-0327-y

DO - 10.1007/s13218-014-0327-y

M3 - Article

SN - 1610-1987

VL - 28

SP - 297

EP - 304

JO - Künstliche Intelligenz

JF - Künstliche Intelligenz

ER -

The RACE Project: Robustness by Autonomous Competence Enhancement

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