Résumé : In this thesis, we use evolutionary robotics techniques to automatically design and synthesise

behaviour for groups of simulated and real robots. Our contribution will be on

the design of non-trivial individual and collective behaviour; decisions about solitary or

social behaviour will be temporal and they will be interdependent with communicative

acts. In particular, we study time-based decision-making in a social context: how the

experiences of robots unfold in time and how these experiences influence their interaction

with the rest of the group. We propose three experiments based on non-trivial real-world

cooperative scenarios. First, we study social cooperative categorisation; signalling and

communication evolve in a task where the cooperation among robots is not a priori required.

The communication and categorisation skills of the robots are co-evolved from

scratch, and the emerging time-dependent individual and social behaviour are successfully

tested on real robots. Second, we show on real hardware evidence of the success of evolved

neuro-controllers when controlling two autonomous robots that have to grip each other

(autonomously self-assemble). Our experiment constitutes the first fully evolved approach

on such a task that requires sophisticated and fine sensory-motor coordination, and it

highlights the minimal conditions to achieve assembly in autonomous robots by reducing

the assumptions a priori made by the experimenter to a functional minimum. Third, we

present the first work in the literature to deal with the design of homogeneous control

mechanisms for morphologically heterogeneous robots, that is, robots that do not share

the same hardware characteristics. We show how artificial evolution designs individual

behaviours and communication protocols that allow the cooperation between robots of

different types, by using dynamical neural networks that specialise on-line, depending on

the nature of the morphology of each robot. The experiments briefly described above

contribute to the advancement of the state of the art in evolving neuro-controllers for

collective robotics both from an application-oriented, engineering point of view, as well as

from a more theoretical point of view.