Information Flow in Animal-Robot Interactions

Abstract

Abstract:The nonverbal transmission of information between social animals is a primary driving force behind their actions and, therefore, an important quantity to measure in animal behavior studies. Despite its key role in social behavior, the flow of information has only been inferred by correlating the actions of individuals with a simplifying assumption of linearity. In this paper, we leverage information-theoretic tools to relax this assumption. To demonstrate the feasibility of our approach, we focus on a robotics-based experimental paradigm, which affords consistent and controllable delivery of visual stimuli to zebrafish. Specifically, we use a robotic arm to maneuver a life-sized replica of a zebrafish in a predetermined trajectory as it interacts with a focal subject in a test tank. We track the fish and the replica through time and use the resulting trajectory data to measure the transfer entropy between the replica and the focal subject, which, in turn, is used to quantify one-directional information flow from the robot to the fish. In agreement with our expectations, we find that the information flow from the replica to the zebrafish is significantly more than the other way around. Notably, such information is specifically related to the response of the fish to the replica, whereby we observe that the information flow is reduced significantly if the motion of the replica is randomly delayed in a surrogate dataset. In addition, comparison with a control experiment, where the replica is replaced by a conspecific, shows that the information flow toward the focal fish is significantly more for a robotic than a live stimulus. These findings support the reliability of using transfer entropy as a measure of information flow, while providing indirect evidence for the efficacy of a robotics-based platform in animal behavioral studies.

Information Dynamics in the Interaction between a Prey and a Predator Fish

“…It can be imagined that the fish may realize the experimental setup forbidding their confrontation in the observation time and adjust their behaviors to this learning. One way to eliminate this effect is to shorten the duration of each trial and to conduct more trials at the same time to achieve statistical significance [19].…”

“…The TE approach now has been used in measuring information flow in the physiology process of neuron dynamics in brain [14,15]. It has also been used in measuring information flow in the information transmitted in a flock in a dynamic process [7] and between a fish and a robot replica [19,20].…”

“…Recently, Butail et al [19] studied the interaction between a zebrafish and a life-sized replica and between two live fish. The trajectory data were recorded when the fish swam in two separated regions in a tank.…”

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

“…In the case where the robots were programmed to only swim in one direction, they influenced the swimming direction of the fish, and thus, the collective choices of the whole mixed group changed, showing that the robots were able to modulate the fishes' behaviors. Further analysis could be made in the future to quantify the interactions between the artificial and the living agents using information theoretic methods as it is shown in Butail et al (2014a).…”

“…In the past decade, researchers in the field of animal-robot interaction have tried to extend this field of study to fish. Four major types of robotic devices have been created for fish-robot interaction studies: a two-dimensional moving platform underneath a tank to transmit the two-dimensional motions to a lure inside the tank using magnetic coupling, as shown in Faria et al (2010); robotic arms that steer lures inside aquariums, as shown in Phamduy et al (2014), Polverino and Porfiri (2013a, b), Kopman et al (2013), Abaid et al (2012), Butail et al (2014a), Cianca et al (2013), Ladu et al (2015a, b), Polverino et al (2012), Spinello et al (2013), Bartolini et al (2016), Donati et al (2016), Ruberto et al (2016Ruberto et al ( , 2017, and Romano et al (2017); wheeled mobile robots that move below a tank and steer lures inside the tank using magnetic coupling, as shown in Swain et al (2012), Rashid et al (2012), and Landgraf et al (2013Landgraf et al ( , 2016; robotic lures that swim autonomously underwater, as shown in Abaid et al (2013), Butail et al (2013), and Butail et al (2014b). While these studies have demonstrated the potential to develop artificial devices able to interact with fish, there is no solution involving multiple robots that move independently and reproduce the same trajectory and locomotion patterns as the fish being studied, which would show how a group of robotic agents would integrate and be able to modulate the collective decision-making process of the animals, as was the case in the LEURRE project.…”