United States

Social robots are increasingly finding applications in sectors such as industry, retail, and healthcare. They employ a combination of verbal and non-verbal cues in order to allow an empathetic and efficient interaction with the humans. In contrast to traditional robots, social robots possess contextual awareness, enabling intelligent responses to human interactions. Within this context, in this paper we propose a framework for social robots, integrating advanced audio and video analytics capabilities with a novel engagement algorithm, designed and developed to facilitate effective communication in multi-user settings. Furthermore, other than conversation skills, the proposed social robot is enhanced with the capability to interactively play games with the human. The effectiveness of the proposed system was tested in real-world scenarios, during two trade shows in Italy, providing valuable insights into its performance and adaptability to different contexts and different audiences.

IEEE RO-MAN 2024

Empowering Human Interaction: A Socially Assistive Robot for Support in Trade Shows

artificial vision

cognitive robotics

deep learning

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

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IEEE RO-MAN 2022

31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

technical paper

[![](https://assets.underline.io/markdown_image/1/image/f25361a85b7c83a7476c0b3c1c0bb1b2.png)](https://drive.google.com/file/d/18jcAcciCBQHj6R07Dbpm2HCOBEIwAOCU/view?usp=sharing)


Dear Colleagues and Friends,

The 33rd IEEE International Conference on Robot and Human Interactive Communication, IEEE RO-MAN 2024, was held at Pasadena Convention Center in Pasadena, USA.

The theme of the IEEE RO-MAN 2024 was **“Embracing Human-Centered HRI”**.

With the continuous technological effort of understanding human needs, creating human-desired robotic systems, and developing real human user experiences, we are marching together towards building a promising and excellent human quality of life, all for our wellness and our next generation.

The conference provided a forum for researchers and practitioners to discuss a variety of topics related to the broad theme of Robot and Human Interactive Communication involving theories, methodologies, technologies, empirical and experimental studies, and generated more insights for the future of Robot and Human Interactive Communication!

We hope you continue to enjoy the post-conference content on Underline!

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The 33rd IEEE International Conference on Robot and Human Interactive Communication was held in Pasadena, California, USA. The theme of the IEEE RO-MAN 2024 was “Embracing Human-Centered HRI”.

With more robots being deployed in the world, users will likely interact with multiple robots sequentially when receiving services. In this paper, we describe an exploratory field study in which unsuspecting participants experienced a ``person transfer'' -- a scenario in which they first interacted with one stationary robot before another mobile robot joined to complete the interaction. In our 7-hour study spanning 4 days, we recorded 18 instances of person transfers with 40+ individuals. We also interviewed 11 participants after the interaction to further understand their experience. We used the recorded video and interview data to extract interesting insights about in-the-field sequential human-robot interaction, such as mobile robot handovers, trust in person transfer, and the importance of the robots' positions. Our findings expose pitfalls and present important factors to consider when designing sequential human-robot interaction.

Person Transfer in the Field: Examining Real World Sequential Human-Robot Interaction between Two Robots

Science fiction has long promised a future within which robots assist humans in many facets of their daily lives, and robot technology is advancing at a pace which suggests that the necessary technology already exists, or may exist, in the near future. But, once the technology is in place, how accepting will humans be to autonomous machines performing tasks traditionally performed by humans? Are we designing and developing robots that are human centric? In a study involving 357 undergraduate students, we found that acceptance of robots was dependent upon previous exposure to different forms of technology (i.e., robots, avatars, video games). Men were more likely to have previous exposure to technology, and were therefore more likely to accept robots and avatars in different tasks compared to women. Enhancing the acceptability of robots by both men and women will require an increased exposure to technology, and women may require additional experience with technology to close the technology acceptance gap.

Technology Exposure Elicits Increased Acceptance of Autonomous Robots and Avatars

Robots are inherently imperfect, and collaborating with an error-prone robotic teammate can deteriorate perceptions of trust and the willingness of users to continue working with the robot. Evidence-based trust repair strategies can be implemented into a robots design to mitigate the decline of trust in human-robot relationships following errors. It is not yet clear what trust repair strategies are most effective. To address this shortcoming, this study investigates two novel trust repair strategies: offered and automatic physical repair. A between-subjects repeated measures study was performed to determine the extent to which each type of physical trust repair was successful in restoring participants perceptions of trust. The results indicated that, where the no-repair condition experienced a significant decrease in trust score, only the automatic repair was consistently successful in bypassing the trust decline. Detailed analysis showed that participants from the offered repair condition did not view the robot as providing the appropriate information, meaning that the offer itself may have confused them. Participants response rate to the Multi- Dimensional Measure of Trust also revealed that users were less willing to associate moral terms with robotic teammates, though this hesitancy may reduce over time. These results contribute to research on human-robot trust repair by uncovering that physical repair is effective when it is automatic, but not when it is offered. This finding will help to further elucidate what repair strategies work to mitigate trust decline and thus help inform robot design.

Robots That Use Physical Repair Strategies after Repeated Errors to Mitigate Trust Decline in Human-Robot Interaction: A Repeated Measures Experiment

This paper demonstrates the effectiveness of learning based models for accurate, and reliable robot to human handoffs in various HRI scenarios. Specifically we bench marked a neuromorphic spiking neural network and a time series k-nearest neighbors classifier against traditional hand crafted force threshold methods. These models use linear force in the x, y, and z direction, as well as torque about the x, y, and z axis at the end effector of the robot arm to make handoff predictions. This paper demonstrates that these learning based methods are more robust to noise which occurs during operational use. We applied our algorithms to both stationary handoffs (stationary robot) and moving handoffs (robot walking). We believe that our evaluation is the first to examine walking handoffs. We evaluated all models in tests which determined the accuracy, precision, recall, f1, and average execution time for handoff events, noise events, and no event tests. We find that the SLAYER spiking neural network model performed the best across both walking and stationary handoffs for the majority of the evaluation criteria. Our results suggest that neuromorphic spiking neural networks are strong contenders for applications in time series, event based HRI applications.

Spiking Neural Networks for Improved Robot-Human Handoffs

Using a dual-task paradigm, we explore how robot actions, performance, and the introduction of a secondary task influence human trust and engagement. In our study, a human supervisor simultaneously engages in a target-tracking task while supervising a mobile manipulator performing an object collection task. The robot can either autonomously collect the object or ask for human assistance. The human supervisor also has the choice to rely on or interrupt the robot. Using data from initial experiments, we model the dynamics of human trust and engagement using a linear dynamical system (LDS). Furthermore, we develop a human action model to define the probability of human reliance on the robot. Our model suggests that participants are more likely to interrupt the robot when their trust and engagement are low during high-complexity collection tasks. Using Model Predictive Control (MPC), we design an optimal assistance-seeking policy. Evaluation experiments demonstrate the superior performance of the MPC policy over the baseline policy for most participants.

Assistance-Seeking in Human-Supervised Autonomy: Role of Trust and Secondary Task Engagement

While robots have advanced in understanding their environments, collaborative tasks demand a deeper comprehension of human intentions to mitigate uncertainty. Anticipatory and proactive behaviours are pivotal in enhancing Human-Robot Interactions (HRI), yet literature often conflates these terms. This study elucidates the distinction between anticipation and proactivity, offering clear definitions and exemplifying their implications through a handover scenario. Through a user study with 24 volunteers performing a total of 72 experiments, we have found that humans are able to distinguish both behaviours and that there is a statistically significant increase in the anthropomorphism of the robot when it behaves proactively. Additionally, both anticipation and proactivity show statistically significant increases in multiple aspects of effective HRI (fluency, comfort, performance, etc.). However, no clear preference for either has been detected.

Anticipation and Proactivity. Unraveling Both Concepts in Human-Robot Interaction through a Handover Example

To enable a comprehensive human-robot interaction, it is essential to refer to human-human collaboration and decode complex non-verbal communication aspects that are essential for adaptive decision-making and task success. Indeed, for a robust collaboration, it is useful to understand the intricacies and complexities of human communication during human-human interaction and to compare it with the human-robot interaction case. We study this communication exchange and information flow by evaluating the leader/follower behavior during physical interaction with different agents and different control types, focusing on non-verbal cues, to identify collaborative or competitive attitudes. To achieve this, we consider a dynamic task of collaboratively catching a falling object, which, by its nature, favors non-verbal communication channels. Multiple subjects performed the same task with different collaborative agents (i.e., human and robot) and with different control modalities, to evaluate the leadership roles and their implication on task success (successfully catching the object while minimizing impact forces). We analyze how the impact force minimization induced by the velocity matching optimal planner affects the catching success rate. The information flow is analyzed, and the leadership roles are identified. Further qualitative data is gathered from questionnaires and compared with respect to the analytic results.

Evaluating Leadership Roles in Human-Robot Interaction Via Highly Dynamic Collaborative Tasks

This paper investigates how conversational agents that are embedded in smart environments should present themselves socially. In an online study, we simulate a future space habitat in which astronauts (participants) interact with one or more agents to complete several tasks related to science, maintenance, and inventory. We examine effects of agent affiliation (affiliation with a user, affiliation with a domain, or affiliation with all users and all domains) and narrative perspective (first- vs. third-person references to parts of the environment) on mental models of the smart environment as one or multiple entities, trust, performance, and social variables. Our findings suggest that in this type of setting, interacting with a single agent may increase mental demand, and that agents that speak about embodied interaction in third person are perceived as more trustworthy and competent than agents that speak in first person.

Contrasting Affiliation and Reference Cues for Conversational Agents in Smart Environments

This study explored how a table-top robot as a recipient of explanations can be applied to the education of students with autism spectrum disorder and intellectual disability. Four 17-year-old participants were recruited from a special needs school. We observed their engagement in assigned tasks and any significant interactions with the robot during the three personalized learning sessions in the school environment. During sessions without a robot, each student was asked to solve arithmetic questions related to purchasing items. During sessions with a robot, each student was required to not only solve questions but also explain the processes or results of simple calculations to the robot. We examined the behavioral, affective, and cognitive engagement of each participant during personalized learning sessions. The results showed that participants solved problems faster, more accurately, or with fewer distractions in the with-robot conditions compared to the task-only conditions. It suggests that this approach, where students explain what they know to a robot, might be effectively adopted for students in special needs schools. Future research should examine the role of robots more comprehensively, acknowledging the limitations of this study, such as the measurement of only three sessions.

Students with Autism Spectrum Disorder and Intellectual Disability Teach a Robot

The landscape of education with social robots is evolving, especially within the realm of music education. However, past studies have focused on children as music learners and such verbal behaviors as praise. Therefore, it remains unknown whether existing studies are effective for adult learners in musical education as well as how to effectively design the non-verbal behaviors of robots. This study investigates the effective behaviors of social robots by comparing three kinds of listening behaviors: none, nodding (simple listening), and enjoying (affective listening). We developed a music education support system that consists of a social robot and a MIDI keyboard and conducted an experiment with adult participants. Our experimental results described the advantages of affective-listening behavior during music education over simple listening and non-listening based on gender.

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Person Transfer in the Field: Examining Real World Sequential Human-Robot Interaction between Two Robots

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