Maria Lombardi

Maria Lombardi, Davide Liuzza, Mario di Bernardo

Designing artificial cyber-agents able to interact with human safely, smartly and in a natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of co-operatively interacting with people in order to fulfil common joint tasks in a multitude of different applications. This is particularly relevant in the context of healthcare applications. Indeed, the use has been proposed of artificial agents interacting and coordinating their movements with those of a patient suffering from social or motor disorders. Specifically, it has been shown that an artificial agent exhibiting certain kinematic properties could provide innovative and efficient rehabilitation strategies for these patients. Moreover, it has also been shown that the level of motor coordination is enhanced if these kinematic properties are similar to those of the individual it is interacting with. In this paper we discuss, first, a new method based on Markov Chains to confer "human motor characteristics" on a virtual agent, so as that it can coordinate its motion with that of a target individual while exhibiting specific kinematic properties. Then, we embed such synthetic model in a control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behaviour of a specific human performing a joint motor task with one or more individuals.

Maria Lombardi, Davide Liuzza, Mario di Bernardo

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Lifting an object together, sawing a wood log, transferring objects from a point to another are all examples where motor coordination between humans and machines is a crucial requirement. While the dyadic coordination between a human and a robot has been studied in previous investigations, the multi-agent scenario in which a robot has to be integrated into a human group still remains a less explored field of research. In this paper we discuss how to synthesise an artificial agent able to coordinate its motion in human ensembles. Driven by a control architecture based on deep reinforcement learning, such an artificial agent will be able to autonomously move itself in order to synchronise its motion with that of the group while exhibiting human-like kinematic features. As a paradigmatic coordination task we take a group version of the so-called mirror-game which is highlighted as a good benchmark in the human movement literature.

Francesco Alderisio, Maria Lombardi, Gianfranco Fiore et al.

Movement coordination in human ensembles has been studied little in the current literature. In the existing experimental works, situations where all subjects are connected with each other through direct visual and auditory coupling, and social interaction affects their coordination, have been investigated. Here, we study coordination in human ensembles via a novel computer-based set-up that enables individuals to coordinate each other's motion from a distance so as to minimize the influence of social interaction. The proposed platform makes it possible to implement different visual interaction patterns among the players, so that participants take into consideration the motion of a designated subset of the others. This allows the evaluation of the exclusive effects on coordination of the structure of interconnections among the players and their own dynamics. Our set-up enables also the deployment of virtual players to investigate dyadic interaction between a human and a virtual agent, as well as group synchronization in mixed teams of human and virtual agents. We use this novel set-up to study coordination both in dyads and in groups over different structures of interconnections, with and without virtual agents. We find that, in dual interaction, virtual players manage to interact with participants in a human-like fashion, thus confirming findings in previous work. We also observe that, in group interaction, the level of coordination among humans in the absence of direct visual and auditory coupling depends on the structure of interconnections among participants. This confirms, as recently suggested in the literature, that different coordination levels are achieved over diverse visual pairings in the presence and in the absence of social interaction. We present preliminary experimental results on the effect on group coordination of deploying virtual computer agents in the human ensemble.