Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 13, Issue 2, Pages 321-332Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2008.927236
Keywords
Collective robotics; evolutionary robotics; self-modeling
Funding
- NASA [NNA04CL10A]
- National Science Foundation (NSF) [DMI (0547376]
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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: it robot can diagnose and recover from unanticipated situations faster by drawing on the previous experiences of the other robots.
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