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.

In this study, we developed a setup allowing two participants ("operators") to jointly control a single humanoid robot body. Specifically, each operator controlled one robot arm, using their anatomically congruent hand (left hand controlling a left robot arm and vice versa). In our task participants had to move each robot arm into one of two possible positions (arm raised or lowered). We used this setup to investigate (1) whether presenting prior information about the relationship between movements performed by each participant ("We-information") can facilitate performance in this task, (2) how joint control over a robot body affects sense of control over the robot and sense of joint agency with the other operator, and (3) how it influences the perceived boundaries between oneself and the others (the robot and the other operator), the so-called "self-other" overlap. We found that (1) "We-information" increased the speed of task performance, but only for simpler configurations, (2) participants experienced high level of sense of control over the robot which increased throughout the task, and (3) a short session of joint control over a humanoid led to pronounced increase in self-other overlap (blurring of boundaries) with both the robot and a co-operator. We discuss implications of our results for understanding of human body representation and how they can inform future applications, such as exoskeletons for individuals affected with hemiplegia.

This article describes a study investigating the effects of decision-making autonomy of a robot, which is teleoperated in a simulated unstable or dangerous environment. It specifically focuses on robot’s autonomy to disregard the user’s command if the robot finds an alternative method of achieving the same goal as pursued by the user, but with significantly reduced risk of failure. Such autonomous control module might prove especially useful under circumstances where human operators cannot access or process all available information quickly enough to make the most optimal decision. We conducted an experiment in which subjects participated in a task of teleoperating either a robot that possesses such autonomous cognitive module, or not. We found that such module significantly reduced their sense of agency over the robot as well as the sense of doing the task together with the robot (sense of joint agency). Most interestingly, it reduced their subjective ratings of performance in the task, when in fact their actual performance was better with such an autonomous robot. A further analysis revealed that this counterintuitive finding was due to an effect of bias: in our study loss of control associated with operating an autonomous robot on average lowered the reported subjective performance by 11% points, with other factors staying equal. These results suggest that this type of assistive autonomy can be beneficial for performance, but might lead to unwanted effects that need to be overcome in order for such system to prove useful in practical applications.

With the increasing use of social robots and automated machines in our daily lives, roboticists need to design robots that are suitable for human-robot collaboration. Prior work suggests that robots that are perceived to be intentional (i.e., are able to experience mental life capacities), can, in most cases, positively affect human-robot collaboration. With studies highlighting the importance of individual differences and how they drive our perception. We aimed to investigate how individual differences in gender moderate the relationship between subjective perceptions of robots and behavioral performance in a human-robot collaborative task. Participants rated a humanoid robot (i.e., iCub) on whether it can experience mental life capacities and completed a collaborative task with it. We correlated their subjective ratings with the completion time of the collaborative task and found a positive correlation between perceiving iCub to experience basic and social emotion with their performance (i.e., movement times). This relationship, however, was evident for males but not females. The results of this study suggest that perceiving humanoid robots as capable of experiencing mental states influences collaborative performance differently depending on gender. These findings can be relevant for the field of social robotics and to successfully design robot interaction partners for workplaces.

When humans interact with artificial agents, they adopt various stances towards them. On one side of the spectrum, people might adopt a mechanistic stance towards an agent and explain its behavior using its functional properties. On the other hand, people can adopt the intentional stance towards artificial agents and explain their behavior using mentalistic terms and explain the agents’ behavior using internal states (e.g., thoughts and feelings). While studies continue to investigate under which conditions people adopt the intentional stance towards artificial robots, here, we report a study in which we investigated the effect of social framing during a color-classification task with a humanoid robot, iCub. One group of participants were asked to complete the task with iCub, in collaboration, while the other group completed an identical task with iCub and were told that they were completing the task for themselves. Participants completed a task assessing their level of adoption of the Intentional Stance (the InStance test) prior to - and after completing the task. Results illustrate that participants who "collaborated" with iCub were more likely to adopt the intentional stance towards it after the interaction. These results suggest that social framing can be a powerful method to influence the stance that people adopt towards a robot.

In this study, we examined whether the likelihood of attributing intentionality to robots is influenced by the human-likeness of errors during HRI. To this end, we designed an experimental protocol in which users performed a melody in a joint task with the iCub robot. We programmed the iCub robot to make an error in 60% of the repetitions. For half of the users, in the erroneous trials, the robot displayed a human-like error, i.e. switched one element of the melody by pressing the wrong key. For the other half of users, the robot erred mechanically, i.e., it interrupted to play the melody and moved back and forth between two keys in an "endless" loop. Before and after the joint musical task, we administered the InStance Test to evaluate the likelihood of treating the robot as an intentional agent. Results showed that mechanical errors during HRI reduced intentionality attribution toward the robot.

Understanding the human cognitive processes involved in the interaction with artificial agents is crucial for designing socially capable robots. During social interactions, humans tend to explain and predict others' behavior adopting the intentional stance, that is, assuming that mental states drive behavior. However, the question of whether humans would adopt the same strategy with artificial agents remains unanswered. The present study aimed at identifying whether the type of behavior exhibited by the robot has an impact on the attribution of mentalistic explanations of behavior. We employed the Instance Questionnaire (ISQ) pre and post-observation of two types of behavior (decisive or hesitant). The ISQ probes participants' stance towards a humanoid robot by requiring them to choose the likelihood of an explanation (mentalistic vs. mechanistic) of iCub depicted in sequences of photographs. We found that decisive behavior, with rare and unexpected "hesitant" behaviors, lead to more mentalistic attributions relative to primarily hesitant behavior. Findings suggest that higher expectations regarding the robots' capabilities and unexpected actions might lead to more mentalistic descriptions.

In this paper we present the TobiiGlassesPySuite, an open-source suite we implemented for using the Tobii Pro Glasses 2 wearable eye-tracker in custom eye-tracking studies. We provide a platform-independent solution for controlling the device and for managing the recordings. The software consists of Python modules, integrated into a single package, accompanied by sample scripts and recordings. The proposed solution aims at providing additional methods with respect to the manufacturer's software, for allowing the users to exploit more the device's capabilities and the existing software. Our suite is available for download from the repository indicated in the paper and usable according to the terms of the GNU GPL v3.0 license.

In this study, we examined if humans adapt their performance to delays in robot's actions in a leader-follower interaction scenario. Participants were asked to “teach” a sequence of musical tones to the iCub robot. The robot repeated the sequence with decreasing delay between its own taps and taps performed by the participants. We observed that mean period of participants' tapping behavior was affected by the iCub's performance. This suggests that humans are sensitive to subtle parameters in robot's behavior and they adapt to them in leader-follower contexts.

The present study aims at investigating how eye contact established by a humanoid robot affects engagement in human-robot interaction (HRI). To this end, we combined explicit subjective evaluations with implicit measures, i.e. reaction times and eye tracking. More specifically, we employed a gaze cueing paradigm in HRI protocol involving the iCub robot. Critically, before moving its gaze, iCub either established eye contact or not with the user. We investigated the patterns of fixations of participants' gaze on the robot's face, joint attention and the subjective ratings of engagement as a function of eye contact or no eye contact. We found that eye contact affected implicit measures of engagement, i.e. longer fixation times on the robot's face during eye contact. Moreover, we showed that joint attention was elicited only when the robot established eye contact, whereas no joint attention occurred when it did not. On the contrary, explicit measures of engagement with the robot did not vary across conditions. Our results highlight the value of combining explicit with implicit measures in an HRI protocol in order to unveil underlying human cognitive mechanisms, which might be at stake during the interactions. These mechanisms could be crucial for establishing an effective and engaging HRI, and provide guidelines to the robotics community with respect to better robot design.

The present study highlights the benefits of using well-controlled experimental designs, grounded in experimental psychology research and objective neuroscientific methods, for generating progress in human-robot interaction (HRI) research. More specifically, we aimed at implementing a well-studied paradigm of attentional cueing through gaze (the so-called “joint attention” or “gaze cueing”) in an HRI protocol involving the iCub robot. Similarly to documented results in gaze-cueing research, we found faster response times and enhanced event-related potentials of the EEG signal for discrimination of cued, relative to uncued, targets. These results are informative for the robotics community by showing that a humanoid robot with mechanistic eyes and human-like characteristics of the face is in fact capable of engaging a human in joint attention to a similar extent as another human would do. More generally, we propose that the methodology of combining neuroscience methods with an HRI protocol, contributes to understanding mechanisms of human social cognition in interactions with robots and to improving robot design, thanks to systematic and well-controlled experimentation tapping onto specific cognitive mechanisms of the human, such as joint attention.

Robots will soon enter social environments shared with humans. We need robots that are able to efficiently convey social signals during interactions. At the same time, we need to understand the impact of robots' behavior on the human brain. For this purpose, human behavioral and neural responses to the robot behavior should be quantified offering feedback on how to improve and adjust robot behavior. Under this premise, our approach is to use methods of experimental psychology and cognitive neuroscience to assess the human's reception of a robot in human-robot interaction protocols. As an example of this approach, we report an adaptation of a classical paradigm of experimental cognitive psychology to a naturalistic human-robot interaction scenario. We show the feasibility of such an approach with a validation pilot study, which demonstrated that our design yielded a similar pattern of data to what has been previously observed in experiments within the area of cognitive psychology. Our approach allows for addressing specific mechanisms of human cognition that are elicited during human-robot interaction, and thereby, in a longer-term perspective, it will allow for designing robots that are well-attuned to the workings of the human brain.

A challenging goal today is the use of computer networking and advanced monitoring technologies to extend human intellectual capabilities in medical decision making. Modern commercial eye trackers are used in many of research fields, but the improvement of eye tracking technology, in terms of precision on the eye movements capture, has led to consider the eye tracker as a tool for vision analysis, so that its application in medical research, e.g. in ophthalmology, cognitive psychology and in neuroscience has grown considerably. The improvements of the human eye tracker interface become more and more important to allow medical doctors to increase their diagnosis capacity, especially if the interface allows them to remotely administer the clinical tests more appropriate for the problem at hand. In this paper, we propose a client/server eye tracking system that provides an interactive system for monitoring patients eye movements depending on the clinical test administered by the medical doctors. The system supports the retrieval of the gaze information and provides statistics to both medical research and disease diagnosis.