Accelerating Self-Modeling in Cooperative Robot Teams

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Josh C. Bongard",
  title =        "Accelerating Self-Modeling in Cooperative Robot
  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

Genetic Programming entries for Josh C Bongard