Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Evolving mobile robots in simulated and real environments
Artificial Life
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Computational principles of mobile robotics
Computational principles of mobile robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving teamwork and role-allocation with real robots
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Swarm-Bot: A New Distributed Robotic Concept
Autonomous Robots
Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments
Evolutionary Computation
Self-organisation and communication in groups of simulated and physical robots
Biological Cybernetics
Cooperation through self-assembly in multi-robot systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Evolving neural mechanisms for an iterated discrimination task: a robot based model
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
To grip, or not to grip: evolving coordination in autonomous robots
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
Intention recognition promotes the emergence of cooperation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A simple metric for turn-taking in emergent communication
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Robotics and Autonomous Systems
GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Communication is of central importance in collective robotics, as it is integral to the switch from solitary to social behavior. In this article, we study emergent communication behaviors that are not predetermined by the experimenter, but are shaped by artificial evolution, together with the rest of the behavioral repertoire of the robots. In particular, we describe a set of experiments in which artificial evolution is used as a means to engineer robot neuro-controllers capable of guiding groups of robots in a categorization task by producing appropriate actions. The categorization is a result of how robots' sensory inputs unfold in time, and, more specifically, of the integration over time of sensory input. In spite of the absence of explicit selective pressure (coded into the fitness function), which would favor signaling over non-signaling groups, communicative behavior emerges. Post-evaluation analyses illustrate the adaptive function of the evolved signals and show that these signals are tightly linked to the behavioral repertoire of the agents. Signals evolve because communication enhances group performance, revealing a “hidden” benefit for social behavior. This benefit is related to obtaining robust and fast decision-making mechanisms. More generally, we show how processes requiring the categorization of noisy dynamical information might be improved by social interactions mediated by communication. In a further series of experiments, we successfully download evolved controllers onto real s-bots. We discuss the challenges involved in porting neuro-controllers displaying time-based decision-making processes onto real robots. Finally, the beneficial effect of communication is shown to transfer to the case of a real robot, and the robustness of the behavior against inter-robot differences is discussed.