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Accelerating Self-Modeling in Cooperative Robot Teams

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1 Author(s)
Bongard, J.C. ; Dept. of Comput. Sci., Univ. of Vermont, Burlington, VT

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.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:13 ,  Issue: 2 )