We study the reality-gap effect (the effect of the inherent discrepancy between simulation and reality) on the human psychophysiological state, workload and reaction time in the context of an active human-swarm interaction scenario. In our experiments, 37 participants interact with a real robot swarm (i.e., with real robots) and with simulated robot swarms. Our results show that the reality-gap significantly affects the human psychophysiological state, workload and reaction time. Our results also show that conducting a human-swarm interaction experiment in a virtual reality environment can be an alternative to conducting an experiment with robot swarms simulated on a computer screen. These results suggest that virtual reality can mitigate the effect of the reality-gap in human-swarm interaction experiments.

The reality gap is the discrepancy between simulation and reality-the same behavioural algorithm results in different robot swarm behaviours in simulation and in reality (with real robots). In this paper, we study the effect of the reality gap on the psychophysiological reactions of humans interacting with a robot swarm. We compare the psychophysiological reactions of 28 participants interacting with a simulated robot swarm and with a real (non simulated) robot swarm. Our results show that a real robot swarm provokes stronger reactions in our participants than a simulated robot swarm. We also investigate how to mitigate the effect of the reality gap (i.e., how to diminish the difference in the psychophysiological reactions between reality and simulation) by comparing psychophysiological reactions in simulation displayed on a computer screen and psychophysiological reactions in simulation displayed in virtual reality. Our results show that our participants tend to have stronger psychophysiological reactions in simulation displayed in virtual reality (suggesting a potential way of diminishing the effect of the reality gap).

We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, twenty-four participants are exposed sequentially to groups of robots made up of one, three and twenty-four robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human, and that higher numbers of robots provoke a stronger response.

We present two empirical studies on the design of control software for robot swarms. In Study A, Vanilla and EvoStick, two previously published automatic design methods, are compared with human designers. The comparison is performed on five swarm robotics tasks that are different from those on which Vanilla and EvoStick have been previously tested. The results show that, under the experimental conditions considered, Vanilla performs better than EvoStick but it is not able to outperform human designers. The results indicate that Vanilla’s weak element is the optimization algorithm employed to search the space of candidate designs. To improve over Vanilla and with the final goal of obtaining an automatic design method that performs better than human designers, we introduce Chocolate, which differs from Vanilla only in the fact that it adopts a more powerful optimization algorithm. In Study B, we perform an assessment of Chocolate. The results show that, under the experimental conditions considered, Chocolate outperforms both Vanilla and the human designers. Chocolate is the first automatic design method for robot swarms that, at least under specific experimental conditions, is shown to outperform a human designer.

We present an experiment in automatic design of robot swarms. For the first time in the swarm robotics literature, we perform an objective comparison of multiple design methods: we compare swarms designed by two automatic methods - AutoMoDe-Vanilla and EvoStick - with swarms manually designed by human experts. AutoMoDe-Vanilla and EvoStick have been previously published and tested on two tasks. To evaluate their generality, in this paper we test them without any modification on five new tasks. Besides confirming that AutoMoDe-Vanilla is e↵ective, our results provide new insight into the design of robot swarms. In particular, our results indicate that, at least under the adopted experimental protocol, not only does automatic design suffer from the reality gap, but also manual design. The results also show that both manual and automatic methods benefit from bias injection. In this work, bias injection consists in restricting the design search space to the combinations of pre-existing modules. The results indicate that bias injection helps to overcome the reality gap, yielding better performing robot swarms.

The term human-swarm interaction (HSI) refers to the interaction between a human operator and a swarm of robots. In this paper, we investigate HSI in the context of a resource allocation and guidance scenario. We present a framework that enables direct communication between human beings and real robot swarms, without relying on a secondary display. We provide the user with a gesture-based interface that allows him to issue commands to the robots. In addition, we develop algorithms that allow robots receiving the commands to display appropriate feedback to the user. We evaluate our framework both in simulation and with real-world experiments. We conduct a summative usability study based on experiments in which participants must guide multiple subswarms to different task locations

Human-swarm interaction (HSI) consists of bidirectional interaction between a human operator and swarms of autonomous robots. In HSI, a human operator directs robots to carry out tasks. However, in order to direct a swarm of robots, the operator must receive appropriate feedback about what is going on in the swarm. In this paper, we argue that self-organised mechanisms should be responsible for providing feedback in HSI systems, and argue against the current approach that involves an extra ‘intepretation layer’ layer dependent on additional infrastructure and modelling. We present a recent study that we conducted in the field of HSI, in which a human operator had to guide groups of robots to designated task completion zones. Based on this study, we propose some initial steps towards our vision of self-organised feedback.

Interacting with a swarm of robots is an essential need for a human who wants to benefit from swarm robotics. This interaction is more complex than guiding a simple agent because in a swarm, robots all have a different frame of reference. To date, there are only a few studies that focused on human-swarm interaction. In this work, a complete framework for interacting with several swarms is developed. It offers the essential robot behaviour needed for the user to control the swarms. This control must be precise in order to guide the groups accurately in the environment. The robots also have to work collectively so that the user can focus on groups instead of on individuals. The interaction system used in this thesis is based on gesture recognition but can be easily adapted to any other kind of control devices. We proved the feasibility of the frramework by both simulation and real world experiments. We also developed a set of statistical tools to measure the usability of human-swarm interaction systems.