Mirror neurons and synchronization between robots

The discovery of "mirror neurons" in the ventral premotor cortex of the macaque monkey by Rizzolatti and colaborators has generated a genuine impact in Neuroscience. In humans it is impossible to registry the neurophysiological activation of simple neurons but disregarding criticisms (see Alison Gopnik-http://www.slate.com/id/2165123/pagenum/all/-), the influence in many fields of the knowledge (study of social relations, robotics, programming, etc.) is enormous. Privileged witness is the recent work of Barakova, Lourens and Yamaguchi. Barakova and Lourens (2008) design a setup for synchronization and turn-taking behaviour in robots. For it, they connect some aspects of the neuroanatomy of the mirror system in humans with an oscillatory dynamics for neural networks.

In brief, the frontal motor areas receive sensory input from the parietal lobe. Another area is situated in the rostral part of the inferior parietal lobule. Both regions form the mirror neuron system. Besides, the posterior sector of the superior temporal sulcus form a core circuit for imitation. Modelling of the superior temporal sulcus area can be reduced to the influence of the inhibitory neurons, projecting the sensory signals to the inferior parietal lobule, area which is associated with multisensory integration. Each robot has 8 range sensors projecting to the sensory integration area that resembles the functionality of the joined temporal sulcus-inferior parietal areas. The two wheels of the robot project to the sensorimotor integration area, resembling the function of the ventral premotor cortex. Self-organization of rhythmic activity of this system can be simulated by means of endogenous oscillators an so, the change of rate of the phase with the time, is the cycle of the limit cycle oscilation, being the phase periodic over the range.

The experimental setting conceived by Barakova and Lourens presents, in the first place, robots that are taking the role of the follower, in order to establish "mirroring" couplings between the nets that simulate the inferior parietal lobule and premotor ventral areas. Hebbian connections between these nets are modelled such that the interaction behaviour is reflected by the average activation values of unikts over a certain time interval. So, the robot playing the role as a follower, tends to synchronize its motion direction with the motion direction of the leading robot. In the second place, the experiment shows the emergence of turn taking between the robots. The role of the robot, being follower or leader depends on which robot is within the visual field of its partner. The emergent turn taking is expressed by symmetry breaking process after a period of synchronization: the leading robot can become a follower and later the lead can be taken over by it.

Inspired by the mirror system in human beings, Barakova and Lourens simulate very interesting interaction behaviours of following and turn taking such that the mirroring functionality is obtained by means of the selforganization of synchronized neural firing in two robots that share perceptual space. No doubt that many extensions of this work remain to develop and promise great advances in the area of robotics.

The e-pucks and social cooperation

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)

(http://www.nac.unina.it/images/pub/cooperationincorvid.pdf)

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.