AutoMoDe: A novel approach to the automatic design of control software for robot swarms
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We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias–variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules—the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.
KeywordsSwarm robotics Automatic design AutoMoDe Evolutionary robotics
The research leading to the results presented in this paper has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 246939. G. Francesca acknowledges support by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. M. Brambilla, A. Brutschy, and M. Birattari acknowledge support from the Belgian F.R.S.-FNRS. Vito Trianni acknowledges support by the Italian National Research Council (CNR) within the EUROCORES Programme EuroBioSAS of the European Science Foundation. The authors thank the anonymous reviewers for their useful comments.
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