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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 …