Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention and coordinating tasks). Gaze is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowed with social abilities can be seen as sophisticated stimuli to study several mechanisms of human social cognition while increasing engagement and ecological validity. In this context, building a robotic perception system to automatically estimate the human gaze only relying on robot’s sensors is still demanding. Main goal of the article is to propose a learning robotic architecture estimating the human gaze direction in table-top scenarios without any external hardware. Table-top tasks are largely used in experimental psychology because they are suitable to implement numerous face-to-face collaborative scenarios. Such an architecture can provide a valuable support in studies where external hardware might represent an obstacle to spontaneous human behaviour, especially in environments less controlled than the laboratory (e.g., in clinical settings). A novel dataset was also collected with the humanoid robot iCub, including images annotated from 24 participants in different gaze conditions.

A key challenge in human-robot interaction research lies in developing robotic systems that can effectively perceive and interpret social cues, facilitating natural and adaptive interactions. In this work, we present a novel framework for enhancing the attention of the iCub humanoid robot by integrating advanced perceptual abilities to recognise social cues, understand surroundings through generative models, such as ChatGPT, and respond with contextually appropriate social behaviour. Specifically, we propose an interaction task implementing a narrative protocol (storytelling task) in which the human and the robot create a short imaginary story together, exchanging in turn cubes with creative images placed on them. To validate the protocol and the framework, experiments were performed to quantify the degree of usability and the quality of experience perceived by participants interacting with the system. Such a system can be beneficial in promoting effective humanrobot collaborations, especially in assistance, education and rehabilitation scenarios where the social awareness and the robot responsiveness play a pivotal role.

Gaze plays a crucial role in human social behavior. Notably, the same applies also to interactions between humans and robots, as gaze can communicate intentions and express interest or aversion similarly to what happens among humans. Besides the direction of gaze (direct vs. averted gaze), its temporal characteristics, such as duration, significantly affect our perception and interpretation of the other’s behavior. In the context of Human-Robot Interaction (HRI), this is still poorly investigated. Thus, the present study aimed to investigate whether, and how, the duration of the robot direct gaze impacts participants’ attitudes towards robots. To do so, participants observed the humanoid robot iCub, whose direct gaze varied in duration between 1 and 8 seconds. Then, they used three Likert scales to rate to what extent the robot gaze made them feel i) comfortable, ii) trustful, and iii) threatened, with participants’ rating operationalizing their attitudes towards the robot. Results showed that, overall, a positive relationship emerged between the duration of the robot gaze and participants’ attitudes, i.e., longer gaze duration led to higher ratings for all three Likert scales.

Designing artificial avatars able to interact with humans in a safe, smart, and natural way is a current open problem in control. Solving such an issue will allow the design of cyber-agents capable of cooperatively 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 for rehabilitation has been proposed of artificial agents able to interact and coordinate their movements with those of patients suffering from social or motor disorders. Moreover, it has also been shown that the level of motor coordination between the avatar and the human patient is enhanced if the kinematic properties of the avatar’s motion are similar to those of the individual it is interacting with. In this article, we discuss, first, a new method based on Markov chains to confer “human motor characteristics” on the motion of a virtual agent so 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 novel control architecture based on reinforcement learning to synthesize a cyber-agent able to mimic the behavior of a specific human performing a joint motor task with one or more individuals.

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task. Examples include lifting an object together, sawing a wood log, transferring objects from a point to another. While 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, driven by a control architecture based on deep reinforcement learning, able to coordinate its motion in human ensembles. As a paradigmatic coordination task we take a group version of the so called mirror game from the human movement literature.

Leadership is a complex yet fascinating phenomenon crucial in many application where coordination of multi-agent systems is involved. This is particularly true in the case of animal groups and other natural systems including human ensembles where, for example in orchestras, a leader is often selected to steer the group towards some desired coordinated behaviour. In this paper we study the emergence of leadership in networks of heterogeneous Kuramoto oscillators, which offer a good model for capturing and describing coordination in human groups performing a joint oscillatory task. We present a mathematical framework to establish phase leadership in the network and apply the results to inform some preliminary experimental results in human groups.