Rasmus Munk
Emma Hart
ABSTRACT
Recent research in small populations of Thymio II robots illustrated the relative benefits of populations distinguishing heritable and learning features in robots for a simple obstacle avoidance task. Here we scientifically validate these results by repeating them using a simulation. An additional benefit of this work is to provide confidence in the simulation model. This is important because evolutionary swarm robotics experiments can be very time consuming to run in real robots. Having a reliable simulation allows many more experiments to be run in simulation with only the most interesting results needing to be verified with real robots. We describe the development of a simulation using RoboRobo that's using the same three-tier learning framework that was demonstrated in the real-world. The simulation is shown to replicate the real-world results in terms of illustrating the relative benefits of each type of learning, and if anything, indicates that social learning can be more powerful than orieinallv thought.
- N Bredeche, J. M Montainer, B Wheel, and E Haasdijk. 2013. Roborobo! a fast robot simulator for swarm and collective robotics. (2013), CoRR, abs/1304.2888.Google Scholar
- Stephane Doncieux, Nicolas Bredeche, Jean-Baptiste Mouret, and Agoston E. (Gusz) Eiben. 2015. Evolutionary Robotics: What, Why, and Where to. Frontiers in Robotics and AI 2 (2015), 4.Google ScholarCross Ref
- Jacqueline Heinerman and Massimiliano Rango. 2015. Thymio Swarm Parameters. (2015). https://github.com/ci-group/ThymioGoogle Scholar
- Jacqueline Heinerman, Massimiliano Rango, and A E Eiben. 2015. Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots. (2015).Google Scholar
- Jacqueline Heinerman, Massimiliano Rango, and A E Eiben. 2015. Thymio Swarm Github. (2015). https://github.com/ci-group/ThymioGoogle Scholar
- Sylvain Koos, Jean-Baptiste Mouret, and Stephane Doncieux. 2010. Crossing the reality gap in evolutionary robotics by promoting transferable controllers. In Proceedings of the 12th annual conference on Genetic and evolutionary computation. ACM, 119--126. Google ScholarDigital Library
Index Terms
- Validation of a learning and evolving robot swarm
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