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, Štefan Konečný, Martin Günther, Sebastian Stock, Luis Seabra Lopes, Miguel Oliveira, Gi Hyun Lim, Hamidreza Kasaei, Vahid Mokhtari, Lothar HotzWilfried Bohlken

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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 languageEnglish
Pages (from-to)297-304
Number of pages8
JournalKI - Kunstliche Intelligenz
Issue number4
Early online date21 Sept 2014
Publication statusPublished - Nov 2014


  • Control Architecture
  • Early Integration
  • Object Category
  • Perceptual Memory
  • Plan Execution

ASJC Scopus subject areas

  • Artificial Intelligence


Dive into the research topics of 'The RACE Project: Robustness by Autonomous Competence Enhancement'. Together they form a unique fingerprint.

Cite this