Accelerating Self-Modeling in Cooperative Robot Teams

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Bongard:2009:TEC,

• author = "Josh C. Bongard",
• title = "Accelerating Self-Modeling in Cooperative Robot Teams",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2009",
• volume = "13",
• number = "2",
• pages = "321--332",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Robots, Robot sensing systems, Training data, Sensors, Data models, Service robots, Computational modeling, self-modeling, Collective robotics, evolutionary robotics",
• DOI = "doi:10.1109/TEVC.2008.927236",
• abstract = "One of the major obstacles to achieving robots capable of operating in real-world environments is enabling them to cope with a continuous stream of unanticipated situations. In previous work, it was demonstrated that a robot can autonomously generate self-models, and use those self-models to diagnose unanticipated morphological change such as damage. In this paper, it is shown that multiple physical quadrupedal robots with similar morphologies can share self-models in order to accelerate modeling. Further, it is demonstrated that quadrupedal robots which maintain separate self-modeling algorithms but swap self-models perform better than quadrupedal robots that rely on a shared self-modeling algorithm. This finding points the way toward more robust robot teams: a robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.",

}

Genetic Programming entries for Josh C Bongard

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