This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants.

Robots are no longer just tools—they are becoming social agents that can shape how we engage with culture. This study examines what influences visitors’ perceptions of an autonomous museum guide robot, focusing not only on technical capabilities but also on human-centered factors. In a maritime exhibition, 34 participants interacted with a fully autonomous, LLM-powered robot acting as a museum guide. Using self-report questionnaires, we explored how individual differences - age and prior experience with robots — interacted with experimental conditions to shape participants’ impressions of the robot. Our findings suggest that these personal factors significantly affect how visitors evaluate the robot, suggesting that effective design must reflect the diversity of users’ experiences and expectations. By acknowledging the complexity of human-robot interaction, we move closer to creating robotic guides that are not only functional but also socially attuned.

Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidance and goal-driven motivation. To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences, facilitating generalized reasoning based on knowledge built across different interactions. A preliminary HRI user study and offline experiments with a synthetic dataset validate our approach, demonstrating the system’s ability to manage complex interactions, autonomously drive training tasks, and build and retrieve contextual memories, advancing socially intelligent robotics.

As hybrid interactions between humans and artificial agents become more prevalent, social skills are increasingly essential for autonomous systems. Beyond assisting in various tasks, robots are expected to understand human states and recognize that knowledge and perceptions of the world can differ, influencing overall behavior. This ability is closely tied to motivation, which plays a crucial role in driving autonomous agents’ actions. In this work, we explore the interaction between two intrinsically motivated cognitive autonomous robots with distinct profiles and preferences, utilizing Theory of Mind to infer each other’s motivations. We investigate the conditions under which they successfully collaborate to achieve mutual well-being and the circumstances that hinder cooperation. Our findings indicate that successful interactions emerge when at least one agent prioritizes helping others and when their profiles are aligned, leading to positive outcomes for both.

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.

Social communication involves interpreting nonverbal behaviors, detecting and anticipating others’ actions and intentions. Actions convey not only the goal and motor intention but also the form, i.e., variations in action execution. These variations, termed vitality forms, communicate attitudes during interactions, such as being gentle, calm, vigorous, and rude. Automatic vitality form recognition may have several applications in social robotics, social skills training, and therapy, yet it remains a rarely studied topic. This paper introduces an unsupervised pre-training approach that utilizes 2D-body key point trajectories as input and employs diffusion models to derive more effective features for representing these trajectories. The features learned from the diffusion model’s encoder are utilized to train a multilayer perceptron for vitality form recognition. Experimental analysis showcases the superior performance of the proposed method not only across various videos but also for action classes not encountered during training.

Dangers in modern human society are commonly attributed to the safety of online activities. In the domain of cybersecurity, Social Engineering (SE) relates to how attackers manipulate and coerce their targets into divulging sensitive information. One major problem in designing social engineering defenses is making users aware they are being targeted. In the context of fostering human empowerment and building an inclusive society, we explore the possibility of leveraging social robot companions to provide improved protection for individuals and companies against cybersecurity attacks, specifically focusing on the realm of social engineering (SE) tactics. We asked participants to play an immersive interactive storytelling game, challenging them with risky and social-engineering-related decisions and monitoring their explicit (i.e., decisions) and implicit (i.e., mouse trajectories and facial expressions) behavior. After each decision, the Furhat tabletop robot intervened, always suggesting the not-selected option. We compared two Compliance Gaining Behaviors (CGBs) the robot could use, either leveraging affection with the participants or logical thinking. Overall, Furhat’s interventions increased the acceptance of risky and SE proposals. However, comparing the situations in which the robot tried to convince participants to avoid a social engineering request to those in which it tried to persuade them to accept it, the former was significantly more successful. Also, participants struggled with ignoring Furhat’s advice, as shown by their more uncertain mouse trajectories and negative emotional valence. From the latter results, we trained a Decision Tree model, based on mouse trajectory features only, to predict if participants would change their minds with an accuracy of 64.9%. Such defense mechanisms could help better understand users’ decision-making process in cybersecurity and social engineering, designing more helpful and supportive robot companions.

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.

Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.

Implicit communication plays such a crucial role during social exchanges that it must be considered for a good experience in human-robot interaction. This work addresses implicit communication associated with the detection of physical properties, transport, and manipulation of objects. We propose an ecological approach to infer object characteristics from subtle modulations of the natural kinematics occurring during human object manipulation. Similarly, we take inspiration from human strategies to shape robot movements to be communica-tive of the object properties while pursuing the action goals. In a realistic HRI scenario, participants handed over cups - filled with water or empty - to a robotic manipulator that sorted them. We implemented an online classifier to differentiate careful/not careful human movements, associated with the cups' content. We compared our proposed “expressive” controller, which modulates the movements according to the cup filling, against a neutral motion controller. Results show that human kinematics is adjusted during the task, as a function of the cup content, even in reach-to-grasp motion. Moreover, the carefulness during the handover of full cups can be reliably inferred online, well before action completion. Finally, although questionnaires did not reveal explicit preferences from partici-pants, the expressive robot condition improved task efficiency.

Social robots are expected to become more and more used in the education field. However, in the interaction between children and social robots, how robots are perceived in social contexts is still under investigation. In this exploratory study, we aimed to investigate how children’s expectations and demographical characteristics (N= 53, 9-14 years old) influence their perception of robot NAO during an education training program in schools. MANCOVA analysis conducted over questionnaire data indicates a positive correlation between the acceptance of the robot and the enjoyment of interacting with it. We found evidence that the more students accepted the robot, the more they perceived the group environment positively. Through a Correspondence Analysis, we investigate which are the preferred features of a robot according to the age of participants. The study suggests that a better opinion of robotics is a factor that can improve the learning environment in this specific context. Our exploratory study encourages conducting studies in-the-wild using self-reported measures to understand the implication of Child-Robot Interaction better.

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.

Abstract Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on certain interaction tasks. However, most interactive scenarios do not have performance alone as an end-goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef’s Hat Card Game, and examine how the rivalry modulation changes the agent’s playing style, and how this impacts the experience of human players on the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which affects directly the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artificial rivalry score contribute to our results.

Current facial expression recognition systems de-mand an expensive re-training routine when deployed to different scenarios than they were trained for. Biasing them towards learning specific facial characteristics, instead of performing typical transfer learning methods, might help these systems to maintain high performance in different tasks, but with a reduced training effort. In this paper, we propose Contrastive Inhibitory AdaptatiOn (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations, in particular when compared with state-of-the-art models. In our discussions, we make an in-depth analysis on how the learned high-level facial features are represented, and how they contribute to each indi-vidual dataset characteristics. We finalize our study by discussing how CIAO positions itself within the range of recent findings on non-universal facial expressions perception, and its impact on facial expression recognition research.

Interacting with partners that understand our desire of closeness or space and adapt their behavior accordingly is an important factor in social interaction, since the perception of others is a fundamental prerequisite for reliable interaction. In human-human interaction (HHI), this information can be inferred by a person's attachment style - a person's characteristic way of forming relationships, modulating behavior (i.e ways to give or seek support) and, on a biological level, their hormone dynamics. Enabling robots to understand their partners' attachment style could enhance robot's perception of partners and help them on how adapt behaviors during an interaction. In this direction, we wish to use the relationship between attachment style and cortisol, to equip the humanoid robot iCub with an internal cortisol-inspired framework that allows it to infer the participant's attachment style and drives it to adapt its behavior accordingly.

As social agents, we experience situations in which sometimes we enjoy being involved and others where we desire to withdraw from. Being aware of others’ “comfort towards the interaction” help us enhance our communications, thus this becomes a fundamental skill for any interactive agent (either a robot or an Embodied Conversational Agent (ECA)). For this reason, the current paper considers Comfortability, the internal state that focuses on the person’s desire to maintain or withdraw from an interaction, exploring whether it is possible to recognize it from human non-verbal behaviour. To this aim, videos collected during real Human-Robot Interactions (HRI) were segmented, manually annotated and used to train four standard classifiers. Concretely, different combinations of various facial and upper-body movements (i.e., Action Units, Head Pose, Upper-body Pose and Gaze) were fed to the following feature-based Machine Learning (ML) algorithms: Naive Bayes, Neural Networks, Random Forest and Support Vector Machines. The results indicate that the best model, obtaining a 75% recognition accuracy, is trained with all the aforementioned cues together and based on Random Forest. These findings indicate, for the first time, that Comfortability can be automatically recognized, paving the way to its future integration into interactive agents.

Recognizing human partners is an essential social skill for building personalized and long-term human-robot interactions. However, robots deployed in complex, real-world environments have to face several challenges, such as managing unstructured interactions with multiple users, limited computational resources, and intrinsic and continuous variability of their sensory evidence. To cope with these challenges, we propose a framework to perform autonomous incremental learning for open-set face recognition suitable for unconstrained HRI scenarios. We validated the proposed framework in a real-world experiment, demonstrating its suitability to let the robot autonomously interact with multiple people while creating a labeled database of their faces across various encounters. Furthermore, we evaluated how an off-the-shelf model performed with data gathered from the HRI setting and proposed a fine-tuned model obtained with a transfer learning technique. Analyses about automatic threshold determination and rehearsal methods for memory sampling were also proposed. Our preliminary results suggest that exploiting the first-hand robot's experience could be crucial to ensure better models' performance and, therefore, could be advantageous for the acceptance and effectiveness of social robots in the long run. With this work, we aim to provide insights on continual learning approaches in the HRI field to promote autonomous and personalized solutions meaningful for real-world applications.

In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the increased dynamics of the scenarios due to the opponents' actions, they usually have to understand how to overcome the opponent's strategies. Most of the common solutions, usually based on continual learning or centralized multi-agent experiences, however, do not allow the development of personalized strategies to face individual opponents. In this paper, we propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. The entire model is trained online, using a composed loss based on a contrastive optimization, to learn competitive and multiplayer games. We evaluate our model on a pokemon duel scenario and the four-player competitive Chef's Hat card game. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times. We also present a discussion on the impact of our model, in particular on how well it deals with on specific strategy learning for each of the two scenarios.

We tested the hypothesis that, if a robot apparently invests effort in teaching a new skill to a human participant, the human participant will reciprocate by investing more effort in teaching the robot a new skill, too. To this end, we devised a scenario in which the iCub and a human participant alternated in teaching each other new skills. In the Adaptive condition of the robot teaching phase , the iCub slowed down its movements when repeating a demonstration for the human learner, whereas in the Unadaptive condition it sped the movements up when repeating the demonstration. In a subsequent participant teaching phase , human participants were asked to give the iCub a demonstration, and then to repeat it if the iCub had not understood. We predicted that in the Adaptive condition , participants would reciprocate the iCub’s adaptivity by investing more effort to slow down their movements and to increase segmentation when repeating their demonstration. The results showed that this was true when participants experienced the Adaptive condition after the Unadaptive condition and not when the order was inverted, indicating that participants were particularly sensitive to the changes in the iCub’s level of commitment over the course of the experiment.

The ability to recognize human partners is an important social skill to build personalized and long-term Human-Robot Interactions (HRI). However, in HRI contexts, unfolding in ever-changing and realistic environments, the identification problem presents still significant challenges. Possible solutions consist of relying on a multimodal approach and making robots learn from their first-hand sensory data. To this aim, we propose a framework to allow robots to autonomously organize their sensory experience into a structured dataset suitable for person recognition during a multiparty interaction. Our results demonstrate the effectiveness of our approach and show that it is a promising solution in the quest of making robots more autonomous in their learning process.

Games are often used to foster human partners' engagement and natural behavior, even when they are played with or against robots. Therefore, beyond their entertainment value, games represent ideal interaction paradigms where to investigate natural human-robot interaction and to foster robots' diffusion in the society. However, most of the state-of-the-art games involving robots, are driven with a Wizard of Oz approach. To address this limitation, we present an end-to-end (E2E) architecture to enable the iCub robotic platform to autonomously lead an entertaining magic card trick with human partners. We demonstrate that with this architecture a robot is capable of autonomously directing the game from beginning to end. In particular, the robot could detect in real-time when the players lied in the description of one card in their hands (the secret card). In a validation experiment the robot achieved an accuracy of 88.2% (against a chance level of 16.6%) in detecting the secret card while the social interaction naturally unfolded. The results demonstrate the feasibility of our approach and its effectiveness in entertaining the players and maintaining their engagement. Additionally, we provide evidence on the possibility to detect important measures of the human partner`s inner state such as cognitive load related to lie creation with pupillometry in a short and ecological game-like interaction with a robot.

In this paper, we investigate whether information related to touches and rotations impressed to an object can be effectively used to classify the emotion of the agent manipulating it. We specifically focus on sequences of basic actions (e.g., grasping, rotating), which are constituents of daily interactions. We use the iCube, a 5 cm cube covered with tactile sensors and embedded with an accelometer, to collect a new dataset including 11 persons performing action sequences associated with 4 emotions: anger, sadness, excitement and gratitude. Next, we propose 17 high-level hand-crafted features based on the tactile and kinematics data derived from the iCube. Twelve of these features vary significantly as a function of the emotional context in which the action sequence was performed. In particular, a larger surface of the object is engaged in physical contact for anger and excitement, than for sadness. Furthermore, the average duration of interactions labeled as sad, is longer than for the remaining 3 emotions. More rotations are performed for anger and excitement than for sadness and gratitude. The accuracy of a classification experiment in the case of four emotions reaches 0.75. This result shows that the emotion recognition during hand-object interactions is possible and it may foster development of new intelligent user interfaces.

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.

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditions, a requirement from FER models given the subjective nature of affect perception and understanding. In this paper, we address this problem by formalising the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We perform a series of experiments on different benchmark datasets to demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER.

Human partners are very effective at coordinating in space and time. Such ability is particular remarkable considering that visual perception of space is a complex inferential process, which is affected by individual prior experience (e.g. the history of previous stimuli). As a result, two partners might perceive differently the same stimulus. Yet, they find a way to align their perception, as demonstrated by the high degree of coordination observed in sports or even in everyday gestures as shaking hands. Robots would need a similar ability to align with their partner's perception. However, to date there is no knowledge of how the inferential mechanism supporting visual perception operates during social interaction. In the current work, we use a humanoid robot to address this question. We replicate a standard protocol for the quantification of perceptual inference in a HRI setting. Participants estimated the length of a set of segments presented by the humanoid robot iCub. The robot behaved in one condition as a mechanical arm driven by a computer and in another condition as an interactive, social partner. Even if the stimuli presented were the same in the two conditions, length perception was different when the robot was judged as an interactive agent rather than a mechanical tool. When playing with the social robot, participants relied significantly less on stimulus history. This result suggests that the brain changes optimization strategies during interaction and lay the foundations to design human-aware robot visual perception.

Learning how to adapt to complex and dynamic environments is one of the most important factors that contribute to our intelligence. Endowing artificial agents with this ability is not a simple task, particularly in competitive scenarios. In this paper, we present a broad study on how popular reinforcement learning algorithms can be adapted and implemented to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style. Finally, we pinpoint how the behavior of each agent derives from their learning style and create a baseline for future research on this scenario.

This paper proposes a method based on visual motion primitives to address the problem of action understanding. The approach builds in an unsupervised way a dictionary of kinematic primitives from a set of sub-movements obtained by segmenting the velocity profile of an action on the basis of local minima derived directly from the optical flow. The dictionary is then used to describe each sub-movement as a linear combination of atoms using sparse coding. The descriptive capability of the proposed motion representation is experimentally validated on the MoCA dataset, a collection of synchronized multi-view videos and motion capture data of cooking activities. The results show that the approach, despite its simplicity, has a good performance in action classification, especially when the motion primitives are combined over time. Also, the method is proved to be tolerant to view point changes, and can thus support cross-view action recognition. Overall, the method may be seen as a backbone of a general approach to action understanding, with potential applications in robotics.

Newborn infants are naturally attracted to human faces, a crucial source of information for social interaction. In robotics, acquisition of such information is crucial and social robots should also learn to exhibit such social skill. Deep learning algorithms are valid candidates to address the problem of face localisation. However, a major drawback of these methods is the large amount of data and human supervision needed in the training procedure. In this work, we propose a cognitive architecture to address autonomous learning from raw sensory signals without supervision. We demonstrate the success of our cognitive framework for the task of face localisation. The proposed cognitive architecture builds on existing work and uses audiovisual attention and a proactive stereo vision mechanism to autonomously direct a robot’s attentive focus towards human faces. The gathered information is used to incrementally generate a dataset that can be used to train a state-of-the-art deep network. The learning system imitates the typical learning process of infants and enhances the learning generalization process by leveraging on the interaction experience with people. The integration of HRI with machine learning, inspired by early development in humans, constitutes an innovative approach for improving autonomous learning in robots.

Skilled motor behavior is critical in many human daily life activities and professions. The design of robots that can effectively teach motor skills is an important challenge in the robotics field. In particular, it is important to understand whether the involvement in the training of a robot exhibiting social behaviors impacts on the learning and the experience of the human pupils. In this study, we addressed this question and we asked participants to learn a complex task - stabilizing an inverted pendulum - by training with physical assistance provided by a robotic manipulandum, the Wristbot. One group of participants performed the training only using the Wristbot, whereas for another group the same physical assistance was attributed to the humanoid robot iCub, who played the role of an expert trainer and exhibited also some social behaviors. The results obtained show that participants of both groups effectively acquired the skill by leveraging the physical assistance, as they significantly improved their stabilization performance even when the assistance was removed. Moreover, learning in a context of interaction with a humanoid robot assistant led subjects to increased motivation and more enjoyable training experience, without negative effects on attention and perceived effort. With the experimental approach presented in this study, it is possible to investigate the relative contribution of haptic and social signals in the context of motor learning mediated by human-robot interaction, with the aim of developing effective robot trainers.

One important challenge for roboticists in the coming years will be to design robots to teach humans new skills or to lead humans in activities which require sustained motivation (e.g. physiotherapy, skills training). In the current study, we tested the hypothesis that if a robot teacher invests physical effort in adapting to a human learner in a context in which the robot is teaching the human a new skill, this would facilitate the human's learning. We also hypothesized that the robot teacher's effortful adaptation would lead the human learner to experience greater rapport in the interaction. To this end, we devised a scenario in which the iCub and a human participant alternated in teaching each other new skills. In the high effort condition, the iCub slowed down his movements when repeating a demonstration for the human learner, whereas in the low effort condition he sped the movements up when repeating the demonstration. The results indicate that participants indeed learned more effectively when the iCub adapted its demonstrations, and that the iCub's apparently effortful adaptation led participants to experience him as more helpful.

A social robot that is aware of our needs and continuously adapts its behaviour to them has the potential of creating a complex, personalized, human-like interaction of the kind we are used to have with our peers in our everyday lives. We are interested in exploring how would an adaptive architecture function and personalize to different users when given different initial values of its variables, i.e. when implementing the same adaptive framework with different robot personalities. Would an architecture that learns very quickly outperform a slower but steadier learning profile? To further explore this, we propose a cognitive architecture for the humanoid robot iCub supporting adaptability and we attempt to validate its functionality and test different robot profiles.

Authority and obedience are key regulatory elements in a society. Robots are becoming important part of our world, and are starting to interact in domains in which authority is an important aspect, as healthcare, teaching or law enforcement. Yet, there is little research on how people behave when robots show authority. In particular, although extensive investigations have been carried out on how authority can circumvent people's morality with experiments such as Milgram's or Stanford Prison, almost no research evaluated the effect of robots pushing the limits of people's own morality. This experiment tries to study this aspect by using a robot (geminoid) with the appearance, and thus authority of a famous person, and by pushing the boundaries asking morally controversial requests. The results show that, even though most people hesitate and recognize the requests as socially inappropriate, they obey to robots with authority. This suggests that the authority of the robot can push people to perform tasks usually considered as inappropriate.

editorial Open AccessHumane Robots—from Robots with a Humanoid Body to Robots with an Anthropomorphic Mind Share on Authors: Giulio Sandini Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genoa, Italy Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genoa, ItalyView Profile , Alessandra Sciutti Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genoa, Italy Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genoa, ItalyView Profile Authors Info & Affiliations ACM Transactions on Human-Robot InteractionVolume 7Issue 1May 2018 Article No.: 7pp 1–4https://doi.org/10.1145/3208954Published:16 May 2018 7citation726DownloadsMetricsTotal Citations7Total Downloads726Last 12 Months219Last 6 weeks31 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Mutual synchronization plays a decisive role in effective collaborations in human joint tasks. Interaction between humans and robots need to show similar emergent coordination. To this aim models of human synchronization have recently been ported on collaborative robots with success [1]. However, it is also important to consider under which conditions the human partner is willing to adapt to the robot while performing a joint task. The main research goal of this study is to understand whether the temporal adaptation usually observed during human-human interaction occurs also during human-robot cooperation. We present a collaborative joint task engaging both human subjects and the humanoid robot iCub in pursuing an identical common goal: putting blocks into a box. We examine human action timing, evinced from motion capture data, in order to investigate whether humans adapt their behavior to the robot. We compare a quantitative measure of such adaptation with the subjective evaluation extracted from questionnaires. We observe that on average participants tend to adapt to their robotic partner. Nevertheless, by looking at individual behaviors, only few showed a clear adaptation to its timing, despite the vast majority of the subjects reported to have been influenced by the robot. We conclude discussing the potential factors influencing human adaptability, with the suggestion that the speed of execution of the robot is determinant in the coordination.

The ability to detect potentially interacting agents in the surrounding environment is acknowledged to be one of the first perceptual tasks developed by humans, supported by the ability to recognise biological motion. The precocity of this ability suggests that it might be based on rather simple motion properties, and it can be interpreted as an atomic building block of more complex perception tasks typical of interacting scenarios, as the understanding of non-verbal communication cues based on motion or the anticipation of others’ action goals. In this article, we propose a novel perspective for video analysis, bridging cognitive science and machine vision, which leverages the use of computational models of the perceptual primitives that are at the basis of biological motion perception in humans. Our work offers different contributions. In a first part, we propose an empirical formulation for the Two-Thirds Power Law , a well-known invariant law of human movement, and thoroughly discuss its readability in experimental settings of increasing complexity. In particular, we consider unconstrained video analysis scenarios, where, to the best of our knowledge, the invariant law has not found application so far. The achievements of this analysis pave the way for the second part of the work, in which we propose and evaluate a general representation scheme for biological motion characterisation to discriminate biological movements with respect to non-biological dynamic events in video sequences. The method is proposed as the first layer of a more complex architecture for behaviour analysis and human-machine interaction, providing in particular a new way to approach the problem of human action understanding.

Humans use eye gaze in their daily interaction with other humans. Humanoid robots, on the other hand, have not yet taken full advantage of this form of implicit communication. We designed a passive monocular gaze tracking system implemented on the iCub humanoid robot [Metta et al. 2008]. The validation of the system proved that it is a viable low-cost, calibration-free gaze tracking solution for humanoid platforms, with a mean absolute error of about 5 degrees on horizontal angle estimates. We also demonstrated the applicability of our system to human-robot collaborative tasks, showing that the eye gaze reading ability can enable successful implicit communication between humans and the robot.

Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naïve subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.

It is generally accepted that a robot should exhibit a contingent behavior, adaptable to the needs of each individual user, to achieve a more natural and pleasant interaction. In this paper we have evaluated whether this general rule applies also when the robot plays a leading role and needs to motivate the human partner to keep a certain pace, as during training or teaching. Also among humans, in schools or factories, structured interaction is often guided by a predefined rhythm, which facilitates the coordination of the partners involved and is thought to maximize their efficiency. On the other hand, a pre-established timing forces all participants to adjust their natural speed to the external, sometimes not appropriate, timing requirement. Where does the optimal trade-off between these two paradigms lie? We have addressed this question in a dictation scenario where the humanoid robot iCub plays the role of a teacher and dictates brief English or Italian sentences to the participants. In particular we compare a condition in which the dictation is performed at a fixed timing with a condition in which iCub monitors subjects' gaze to adjust its dictation speed. The results are discussed both in terms of participants' subjective evaluation and their objective performance, by highlighting the advantages and drawbacks of the choice of contingent robot behavior.

Passing an object between two humans is a very natural and seamless operation, mainly thanks to non-verbal cues which facilitate the process. Just from action observation, humans can easily anticipate where and when a passing movement will end and how heavy the transported object is. But how could this natural understanding be ported to non-human agents? We introduce a simple robotic architecture to enable the iCub humanoid robot to visually recognize the weight of an object and select a lift-to-pass motion which implicitly communicates such information to the action partner. In this work we mainly focus on the building and training of the procedural memory module needed to store the association between the mass of an object and its visual appearance, and we propose how such a model can be used to successively select communicative lifting motions.

Knowing where a person is looking is an important parameter of every human-human interaction. Detecting a person's gaze could significantly improve the interaction capabilities of today's robotic agents. But many robots' visual systems are limited by data bandwidth and optical hardware. We propose a low-cost high-def pan/tilt/zoom active vision system that could significantly improve the robot's eye tracking capabilities. We tested the proposed system for improving mutual gaze detection in a human-robot interaction scenario and found significant results compared to systems without zoom capability.

A fundamental challenge for robotics is to transfer the human natural social skills to the interaction with a robot. At the same time, neuroscience and psychology are still investigating the mechanisms behind the development of human-human interaction. HRI becomes therefore an ideal contact point for these different disciplines, as the robot can join these two research streams by serving different roles. From a robotics perspective, the study of interaction is used to implement cognitive architectures and develop cognitive models, which can then be tested in real world environments. From a neuroscientific perspective, robots could represent an ideal stimulus to establish an interaction with human partners in a controlled manner and make it possible studying quantitatively the behavioral and neural underpinnings of both cognitive and physical interaction. Ideally, the integration of these two approaches could lead to a positive loop: the implementation of new cognitive architectures may raise new interesting questions for neuroscientists, and the behavioral and neuroscientific results of the human-robot interaction studies could validate or give new inputs for robotics engineers. However, the integration of two different disciplines is always difficult, as often even similar goals are masked by difference in language or methodologies across fields. The aim of this workshop will be to provide a venue for researchers of different disciplines to discuss and present the possible point of contacts, to address the issues and highlight the advantages of bridging the two disciplines in the context of the study of interaction.

Human interaction is based, among other factors, on non verbal and implicit communication. By observing the action of someone else we can automatically infer several non obvious details of what's happening, as the goal of the agent, his mood and even some features of the object he is using, e.g., if it is heavy or light. This action reading skill is developed very early in life and constitutes a fundamental basis for the development of collaboration. A similar capacity to implicitly communicate weight would be desirable also in a humanoid robot, to allow for a natural preparation for hand-over between the robot and the human partner. Here we have investigated how the ability to infer weight from human action observation develops during childhood and whether such ability generalizes to the observation of a humanoid robot. In particular, the robot was not programmed to perform human-like lifting actions, but just to replicate a property of human lifting deemed as determinant for weight reading: exhibiting a velocity proportional to object weight. Our results suggest that although 6-year-olds can already judge weight from the observation of a lifting action, they cannot generalize this skill to simplified robotic actions as the ones proposed here.

Seeing the world through the eyes of a child is always difficult. Designing a robot that might be liked and accepted by young users is therefore particularly complicated. We have investigated children's opinions on which features are most important in an interactive robot during a popular scientific event where we exhibited the iCub humanoid robot to a mixed public of various ages. From the observation of the participants' reactions to various robot demonstrations and from a dedicated ranking game, we found that children's requirements for a robot companion change sensibly with age. Before 9 years of age children give more relevance to a human-like appearance, while older kids and adults pay more attention to robot action skills. Additionally, the possibility to see and interact with a robot has an impact on children's judgments, especially convincing the youngest to consider also perceptual and motor abilities in a robot, rather than just its shape. These results suggest that robot design needs to take into account the different prior beliefs that children and adults might have when they see a robot with a human-like shape.

This preliminary study looks into how visual information affects motion planning in a gravitational environment. Recently, it has been shown that goal-directed tasks, e.g. simple pointing actions, are performed using a combination of a priori knowledge and closed-loop information, coming from proprioceptive-vestibular and visual feedback. In particular it has been observed that visual information is used by the central nervous system to reorganize motor planning when visuo-motor conflicts occur.The aim of our work is to investigate deeper the contribution of the visual channel in motor planning by means of virtual reality tools. Indeed, virtual reality technology is used here to modify the nature of visual input, leading to test several scenarios from minimalist and impoverished scenes to fully immersive and realistic environments. In fact we want to understand how the richness of the visual information is relevant in influencing our motor planning. We describe here our experimental setup and protocols. Moreover we present some preliminary results and we discuss the ongoing developments.