The social interaction test is based on spontaneous behaviour observed between pairs of animals, usually rodents. Commonly, the behaviour of one of the members in the dyad is related to the behaviour of its partner; therefore, making accurate predictions about behaviour is difficult, and the behaviour of the dyad cannot be controlled. In the present study, we programmed an e-puck robot with simple behavioural patterns, such as moving around a cage and following and approaching a rat. The results were analysed by comparing behaviour that was displayed by two groups of experimental rats towards different types of partners: (i) in the first group of experimental rats, they interacted with another group of rats and (ii) in the second group of experimental rats, they interacted with the e-puck robot. Our aim was to study the behaviour of experimental rats in the social interaction test when the interaction partner is a pre-programmed robot, in order to find out whether a rat is able to display social interaction activities in that context. Those activities were evaluated by a structured scheme of possible behaviours, quantified in categories according to currently well-accepted nomenclature and definitions. In order to achieve this goal, we compared the social and non-social behaviour displayed by the experimental rat in rat-rat and rat-robot interactions (such as approaching and following behaviour). We observed predominantly non-social behaviours, such as exploring the cage, when the experimental rats confronted either another rat or the robot. The experimental rats displayed similar periods of approaching, sniffing and crawling (social behaviour), exploring, being quiet, self-grooming and evading (non-social behaviour) in encountering both the rat and the robot. However, in the presence of the robot, the experimental rats displayed long periods of time spent in following, in contrast to short periods of immobility. In the present study, we explored a behavioural repertoire that was classified into the social and the non-social, in which the robot was usually able to elicit social behaviour from the rat. The results of our experiments open possibilities for additional studies on social interaction in robot-live rat dyads (e.g., in predator-prey models).

Several fields of research such as medicine, robotics, sports, informatics, etc., require the analysis of human movement. Traditional systems for acquisition and analysis of human movement data are based on video cameras or active sensors. However, those systems are limited to high-resource settings. Wearable devices allow monitoring subjects outside typical clinical or research environments. Here, we present an open source low-cost wireless sensor system for acquisition of human movement data. Our system consists of two main parts: a server that stores data and, one or more wearable sensor modules that collect movement data through Inertial Measurement Units (IMUs) and transmit them wirelessly to the server. As a proof of concept, we measured human gait activity. Our results show that our system with IMUs can acquire quantifiable movement data. Characteristics such as open source code and its low-cost, make our system a viable alternative for clinical or research.

The vertebrate basal ganglia play an important role in action selection—the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot’s motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a ‘behavioral trap’. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.

Communication systems represent an evolutionary advantage for a group of robots solving coordinated tasks. In the field of evolutionary robotics, the emergence and establishment of communication are regulated by different variables. These systems are a tool produced by artificial evolution for the exchange of personal and environmental information. Since evolutionary processes involve multi-objective evolutionary methods, it is important to study all the mechanisms that affect the emergence and establishment of communication systems. One of these variables is the evolutionary advantage of emerging signals. In this article it is assumed that the signals appear because they are evolutionarily useful in solving a task. Emitter and receiver generate a process of conceptualization, which makes the signals associated with a meaning for the community. In this way an experiment is adapted to the FARSA simulator and the MARXBOT robot. This experiment consists of a group of robots that spend as much time as possible in a food zone and avoid a poisoned zone. Under normal conditions, robots tend to point to the food zone to attract the rest of the population. When the evolutionary importance of pointing out the food zone is reduced, the signals arise in different situations such as the presence of nearby objects and the poisoned zone. Communication is configured with the LED rings and linear cameras of the robots. In addition, an ethological method is adapted to quantify the behavioral effects of experimental manipulation.

In this paper we focus on the development of a selection model that allows a robot to select pre-evolved behaviour in a motivated environment. A collection task is set in an arena where the Khepera robot has to collect cylinders, which simulate food. Therefore, two basic motivations, labelled as ‘obesity’ and ‘anorexia’, both affect the selection of the behavioural repertoire. Additionally, in this model is possible to modulate the model to be either more or less selective depending on the tonic value of the simulated ‘mesolimbic/cortical tone’. Next, we use evolution for optimizing the motivated selection network employed for behavioural switching. Finally, we describe the results of varying, by hand and evolution, the levels of reward by the mesolimbic-cortical tone.