AutoMoDe: A novel approach to the automatic design of control software for robot swarms
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.
- Berman, S., Kumar V., & Nagpal. R. (2011). Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. In 2011 IEEE International Conference on Robotics and Automation (ICRA) (pp. 378–385). Piscataway, NJ: IEEE Press.Google Scholar
- Birattari, M. (2004). On the estimation of the expected performance of a metaheuristic on a class of instances. How many instances, how many runs? Technical report TR/IRIDIA/2004-001, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.Google Scholar
- Birattari, M., Stützle, T., Paquete, L., & Varrentrapp, K. (2002). A racing algorithm for configuring metaheuristics. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002) (pp. 11–18). San Francisco: Morgan KaufmannGoogle Scholar
- Brambilla, M., Pinciroli, C., Birattari, M., & Dorigo. M. (2012). Property-driven design for swarm robotics. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012) (pp. 139–146). Richland, SC: IFAAMAS.Google Scholar
- Şahin, E. (2005). Swarm robotics: From sources of inspiration to domains of application. In Proceedings of the 2004 International Conference on Swarm Robotics, volume 3342 of LNCS, (pp. 10–20). Berlin, Germany: Springer.Google Scholar
- Dietterich, T., & Kong, E. B. (1995). Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report: Department of Computer Science, Oregon State University.Google Scholar
- Duarte, M., Oliveira, S., & Christensen, A. L. (2012a). Automatic synthesis of controllers for real robots based on preprogrammed behaviors. In From animals to animats 12, volume 7426 of LNCS (pp. 249–258). Berlin, Germany: Springer.Google Scholar
- Duarte, M., Oliveira, S., & Christensen, A. L. (2012b). Hierarchical evolution of robotic controllers for complex tasks. In 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) (pp. 1–6). Piscataway, NJ: IEEE Press.Google Scholar
- Ferrante, E., Guzmán, E. D., Turgut, A. E., & Wenseleers, T. (2013). GESwarm: Grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics. In Proceedings of the Fifteenth International Conference on Genetic and Evolutionary Computation Conference Companion (pp. 17–24). New York: ACM.Google Scholar
- Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., & Birattari. M. (2012). Analysing an evolved robotic behaviour using a biological model of collegial decision making. In From animals to animats 12, volume 7426 of LNCS (pp. 381–390) Berlin, Germany: Springer.Google Scholar
- Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., & Birattari, M. (2013). AutoMoDe: A novel approach to the automatic design of control software for robot swarms. Supplementary information page at http://iridia.ulb.ac.be/supp/IridiaSupp2013-007/
- Gutiérrez, Á., Campo, A., Dorigo, M., Donate, J., Monasterio-Huelin, F., & Magdalena, L. (2009). Open E-puck range & bearing miniaturized board for local communication in swarm robotics. In 2009 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3111–3116). Piscataway, NJ: IEEE Press.Google Scholar
- Jakobi, N., Husbands, P., & Harvey, I. (1995). Noise and the reality gap: The use of simulation in evolutionary robotics. In Advances in Artificial Life (ECAL’95), volume 929 of LNCS (pp. 704–720) Berlin, Germany: Springer.Google Scholar
- Liu, W., Winfield, A., Sa, J., Chen, J., & Dou, L. (2007). Strategies for energy optimisation in a swarm of foraging robots. Swarm Robotics, volume 4433 of LNCS (pp. 14–26). Berlin, Germany: Springer.Google Scholar
- López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., & Birattari, M. (2011). The irace package, iterated race for automatic algorithm configuration. Technical report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.Google Scholar
- Matarić, M. (1997b). Reinforcement learning in the multi-robot domain. Autonomous Robots, 4(1), 73–83.Google Scholar
- Miglino, O., Lund, H. H., & Nolfi, S. (1995). Evolving mobile robots in simulated and real environments. Artificial Life, 2(4), 417–434.Google Scholar
- Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., & Martinoli, A. (2009a). The e-puck, a robot designed for education in engineering. In Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions (pp. 59–65). Castelo Branco, Portugal: IPCB: Instituto Politécnico de Castelo Branco.Google Scholar
- Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.-C., Floreano, D., & Martinoli, A. (2009b). E-puck website. URL http://www.e-puck.org/. Accessed Nov 2013.
- Nolfi, S., & Floreano, D. (2000). Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: MIT Press.Google Scholar
- Pinville, T., Koos, S., Mouret, J.-B., & Doncieux, S. (2011). How to promote generalisation in evolutionary robotics: The ProGAb approach. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (pp. 259–266). New York: ACM.Google Scholar
- R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL http://www.R-project.org.
- Stranieri, A., Turgut, A., Francesca, G., Reina, A., Dorigo, M., & Birattari, M. (2013). IRIDIA’s arena tracking system. Technical report TR/IRIDIA/2013-013, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.Google Scholar