In Evolutionary Robotics (ER), explicitly rewarding for behavioral diversity recently revealed to generate efficient results without recourse to complex fitness functions. The principle of such approaches is to explicitly encourage diversity in the robot behavior space instead of in the space of genotypes (the space explored by the evolutionary algorithm) or the space of phenotypes (the space of robot controllers and morphologies). To implement such approaches, a similarity between behaviors needs to be evaluated but, up to now, used similarity measures are problem-specific. The goal of this work is to explore generic behavioral similarity measures that only rely on sensori-motor values. With such a measure, we managed to evolve the topology and the parameters of neuro-controllers that make a simulated robot go towards a ball, take it, find a basket, put the ball into the basket, perform a half-turn, search and take another ball, put it into the basket, etc. In this experiment, two objectives were simultaneously optimized with NSGA-II: the number of collected balls and the generic behavioral diversity objective. Several generic behavioral measures are compared. To confirm the interpretation of behavioral diversity objective and in an attempt to characterize behavioral similarity measures, they are also compared to human-made behavioral similarity evaluations. They reveal to classify behaviors globally as humans did, but with no clear correlation between the closeness to human classification and the efficiency within an evolutionary run.