The e-pucks are mobile robots developed at the École Polytechnique Fédérale de Lausanne (EPFL). The designers (see Mondada et al., The e-puck, a Robot Designed for Education in Engineering, 2009) looked for the miniaturization of a complex system combining desktop size and flexibility. These robots have sensors in different modalities (distances to objects by means of eight infrared proximity sensors, accelerometer, color camera, microphones), actuators with different actions on the environment, wired and wireless communication devices and two types of processors (general purpose and DSP). Although they were conceived for education in Engineering, they have demonstrated to be very useful for experimentation in Artificial Intelligence.
Social learning is the capability of an organism to learn by observing the behavior of a conspecific. Evolutionary robotics is a methodological tool to design robots´controllers. Recently, Miglino, Ponticorvo and Donetto (2008)
have used an evolutionary algorithm for the neural control of e-puck robots which imitate the cooperation in corvids to obtain a reward (food), which is clearly visible, but not directly reachable. The dyad gets the reward if the two tips of a string are pulled at the same time.
Two e-puck robots are situated in an environment consisting of a square arena and of a corridor both surrounded by walls. The authors use an evolutionary algorithm to set the weights of the robots´neural controller. The initial population consists of 100 randomly generated genotypes that encode the connection weights of 100 corresponding neural networks. Each genotype is translated into 2 identical neural controllers which are embodied in 2 e-pucks. The 20 best genotypes of each generation are reproduced by generating 5 copies each, with 2% of their bits replaced with a new randomly selected value. The evolutionary process is iterated 1000 times and the experiment is replicated 20 times each consisting of 4 trials with 4 different starting positions in the corners of the room. Cooperation between e-pucks is regulated by social interaction with communication as a medium. The emergence of communication leads to a coordinated cooperation behavior similar to cooperation observed in corvids.
Nowadays artificial evolution is employed to build neural mechanisms that control the behavior of learning robots. Mechanisms for social learning in organisms can be successfully simulated in an integrated neural network architecture. E-pucks are an excellent tool for simulating social learning in organisms requiring cognitive mechanisms. But future work is needed, in particular to allow the robots to autonomously decide when to use social learning strategies or individual strategies.
