Understanding and ascribing to others’ intentions and beliefs based on the observed behavior is a key aspect of people’s everyday social lives. This is crucial also in Human-Robot Interaction because both humans and robots need to make sense of each others’ behavior in collaborative settings. Such a complex mechanism is known as the Theory of Mind (ToM), and it still holds secrets, although it has been investigated in HRI for several years.This study focuses on the second-order ToM attributions using an inverted Sally-Anne paradigm, a well-established False Belief task. The humanoid robot Pepper, equipped with vision algorithms, assumes the role of Anne and predicts where Sally (a human researcher) will search for a ball, contingent on Sally’s presence or absence during its relocation by a neutral experimenter. Two scenarios are tested: Sally exits the room (false belief) or observes the relocation (true belief). We had two experimental conditions, where the Pepper robot exhibited passive (monotonic voice, rigid gestures) and active (dynamic voice, fluid gestures) behaviors, respectively. Participants, as external observers, watch video recordings of the interactions and answer structured questions to assess how behavioral cues influence robots’ ToM attributions.Results showed that people tended to ascribe high-level ToM skills to the active robot rather than to the passive one, highlighting the importance of designing robots with appropriate expressive behaviors. By examining how humans interpret a robot’s capacity for second-order ToM, this work advances our understanding of the cognitive assumptions people make about artificial agents and offers a foundation for developing socially intelligent systems that can seamlessly integrate into collaborative environments.

The addressee estimation (understanding to whom somebody is talking) is a fundamental task for human activity recognition in multi-party conversation scenarios. Specifically, in the field of human–robot interaction, it becomes even more crucial to enable social robots to participate in such interactive contexts. However, it is usually implemented as a binary classification task , restricting the robot’s capability to estimate whether it was addressed or not, which limits its interactive skills. For a social robot to gain the trust of humans, it is also important to manifest a certain level of transparency and explainability. Explainable artificial intelligence thus plays a significant role in the current machine learning applications and models, to provide explanations for their decisions besides excellent performance. In our work, we (a) present an addressee estimation model with improved performance in comparison with the previous state-of-the-art; (b) further modify this model to include inherently explainable attention-based segments; (c) implement the explainable addressee estimation as part of a modular cognitive architecture for multi-party conversation in an iCub robot; (d) validate the real-time performance of the explainable model in multi-party human–robot interaction; (e) propose several ways to incorporate explainability and transparency in the aforementioned architecture; and (f) perform an online user study to analyze the effect of various explanations on how human participants perceive the robot.

The "Robots for Humans" (RfH) workshop bridges the Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI) communities. The workshop encourages methodological exchange and explores theoretical, technical and design solutions in robotics. Join us to explore the intricate bond between humans and robots for thoughtful HRI advancements.

In this paper, we investigated how shared experience-based counterfactual explanations affected people's performance and robots' persuasiveness during a decision-making task in a social HRI context. We used the Connect 4 game as a complex decision-making task where participants and the robot had to play as a team against the computer. We compared two strategies of explanation generation (classical vs shared experience-based) and investigated their differences in terms of team performance, the robot's persuasive power, and participants' perception of the robot and self. Our results showed that the two explanation strategies led to comparable performances. Moreover, shared experience-based explanations - based on the team's previous games - gave higher persuasiveness to the robot's suggestions than classical ones. Finally, we noted that low-performers tend to follow the robot more than high-performers, providing insights into the potential danger for non-expert users interacting with expert explainable robots.

In this work, we performed a user study in which participants had to solve a human-robot teaming decision-making task (the Connect 4 game) with an explainable vs non-explainable robot. During the task, the robot provided suggestions and, depending on the experimental condition, explanations to justify those suggestions. We compared participants’ behaviours in interacting with both types of robots. In particular, we investigated how participants’ personality dimensions and previous experiences with the iCub robot impacted participants’ decision-making. We also studied how participants aligned with iCub’s playing style as the interaction continued. Our results show that participants’ negative agency and agreeableness substantially impacted how they accepted the robot’s suggestions when it provided example-based counterfactual explanations. We also observed a learning effect: participants tended to align with the robot’s playing style during the interaction. However, the participants’ learning depended not only on the presence of the explanations, but also on the time spent with the robot. Moreover, the human-robot team’s victories were mainly attributable to the robot’s persuasiveness rather than the participants’ skills in the game.

The high request for autonomous human–robot interaction (HRI), combined with the potential of machine learning (ML) techniques, allow us to deploy ML mechanisms in robot control. However, the use of ML can make robots’ behavior unclear to the observer during the learning phase. Recently, transparency in HRI has been investigated to make such interactions more comprehensible. In this work, we propose a model to improve the transparency during reinforcement learning (RL) tasks for HRI scenarios: the model supports transparency by having the robot show nonverbal emotional-behavioral cues. Our model considered human feedback as the reward of the RL algorithm and it presents emotional-behavioral responses based on the progress of the robot learning. The model is managed only by the temporal-difference error. We tested the architecture in a teaching scenario with the iCub humanoid robot. The results highlight that when the robot expresses its emotional-behavioral response, the human teacher is able to understand its learning process better. Furthermore, people prefer to interact with an expressive robot as compared to a mechanical one. Movement-based signals proved to be more effective in revealing the internal state of the robot than facial expressions. In particular, gaze movements were effective in showing the robot's next intentions. In contrast, communicating uncertainty through robot movements sometimes led to action misinterpretation, highlighting the importance of balancing transparency and the legibility of the robot goal. We also found a reliable temporal window in which to register teachers’ feedback that can be used by the robot as a reward.

Social robots are effective in influencing and motivating human behavior. To gain a deeper understanding of how the robot emotional non-verbal behaviors might shape the human perception of the interaction while providing recommendations, we conducted a between-subjects experimental study using a humanoid robot in a movie recommendation scenario. This experiment aims at evaluating whether an incoherent use of emotional behavior, with respect to the presented contents, may produce a sort of humorous effect that positively affect the user perception of the recommendation. We evaluated, using an off-the-shelf solution, engagement and emotions shown by the users. Our results showed that a robot incoherent behavior does not distract the user, but it increases his/her engagement producing a positive emotional response. Such a difference is significant in the case of female subjects and depends on the considered emotions.