United States

Dialogs between humans and social robots are often fact-oriented and not very mood-dependent, at least on the part of the robot. This raises the question of what influence the tonality of the utterances has on the perceived personality of the robot and how this influences the user&#39;s further expectations. One element that is not necessarily expected from robots in conversation is humor, especially the use of irony and sarcasm. We examined the effect of a conversation with cheerfully ironic or sullenly sarcastic remarks by the robot in contrast to utterances in a more seriously neutral tone. To measure these effects in terms of perceived warmth, competence and possible discomfort in conversation we used the Robotic Social Attributes Scale (RoSAS) on 20 participants with an age ranging from 26 to 58. In addition, we asked the participants for their assessment of the suitability of the robot&#39;s dialog style for different areas of application and which dialog style they personally liked best. The dialog parts of the robot were generated with ChatGPT using suitable prompts. Our results showed that the perceived warmth gained highest rates when when the robot hat a cheerfully ironic tone. Discomfort was rated significantly higher when a sullenly sarcastic tone was used by the robot. Perceived competence seemed to be slightly negatively influenced in the sarcastic condition. The results proved that tonality appears to be a very relevant design element for a successful interaction between humans and robots, but must be used carefully to achieve the desired goal.

IEEE RO-MAN 2024

Introducing a Note of Levity to Human-Robot Interaction with Dialogs Containing Irony, Sarcasm and Jocularity

sarkasm

chatgpt

large language model

irony

social robot

humor

human-robot interaction

**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)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

Registration Is Required

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.

Dialogs between humans and social robots are often fact-oriented and not very mood-dependent, at least on the part of the robot. This raises the question of what influence the tonality of the utterances has on the perceived personality of the robot and how this influences the user's further expectations. One element that is not necessarily expected from robots in conversation is humor, especially the use of irony and sarcasm. We examined the effect of a conversation with cheerfully ironic or sullenly sarcastic remarks by the robot in contrast to utterances in a more seriously neutral tone. To measure these effects in terms of perceived warmth, competence and possible discomfort in conversation we used the Robotic Social Attributes Scale (RoSAS) on 20 participants with an age ranging from 26 to 58. In addition, we asked the participants for their assessment of the suitability of the robot's dialog style for different areas of application and which dialog style they personally liked best. The dialog parts of the robot were generated with ChatGPT using suitable prompts. Our results showed that the perceived warmth gained highest rates when when the robot hat a cheerfully ironic tone. Discomfort was rated significantly higher when a sullenly sarcastic tone was used by the robot. Perceived competence seemed to be slightly negatively influenced in the sarcastic condition. The results proved that tonality appears to be a very relevant design element for a successful interaction between humans and robots, but must be used carefully to achieve the desired goal.

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!

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

It looks like you are not registered for this event. 

A registration is required to access this event page. 



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”.

The goal-directed behavior observed in humans arises from the intricate interplay of various processes, including personality dynamics, emotional responses to others, memory encoding, the anticipation of future actions, and associated hedonic experiences. Integrating these multiple processes characteristic of human intelligence into a robotic framework aims to enhance the human-likeness of artificial agents and facilitate more natural and intuitive interactions with humans.   For this purpose, in this paper, we propose a comprehensive psychological and cognitive architecture where, personality, as it happens for humans, not only influences the execution of actions but also shapes internal reactions to human emotions and guides anticipatory decision-making processes tailored to the agent's traits. We demonstrate the framework's effectiveness in generating perceivable synthetic personalities through an experiment involving participants in a dyadic conversation scenario with a digital human, where the digital human's behavior is driven by its assigned personality. The results show that participants accurately perceive the artificial personality displayed by the digital human. We also demonstrate the potential of our robotic framework to bridge the gap between cognitive and psychological agents, as the findings highlight its ability to create a cognitively and emotionally intelligent digital human.

Personality and Memory-Based Framework for Emotionally Intelligent Agents

To enable sophisticated interactions between humans and robots in a shared environment, robots must infer the intentions and strategies of their human counterparts. This inference can provide a competitive edge to the robot or enhance human-robot collaboration by reducing the necessity for explicit communication about task decisions. In this work, we identify specific states within the shared environment, which we refer to as Critical Decision Points, where the actions of a human would be especially indicative of their high-level strategy. A robot can significantly reduce uncertainty regarding the human's strategy by observing actions at these points. To demonstrate the practical value of Critical Decision Points, we propose a Receding Horizon Planning (RHP) approach for the robot to influence the movement of a human opponent in a competitive game of hide-and-seek in a partially observable setting. The human plays as the hider and the robot plays as the seeker. We show that the seeker can influence the hider to move towards Critical Decision Points, and this can facilitate a more accurate estimation of the hider's strategy. In turn, this helps the seeker catch the hider faster than estimating the hider's strategy whenever the hider is visible or when the seeker only optimizes for minimizing its distance to the hider.

Planning with Critical Decision Points: Robots That Influence Humans to Infer Their Strategy

Despite the rise of mobile robot deployments in home and work settings, perceived safety of users and bystanders is understudied in the human-robot interaction (HRI) literature. To address this, we present a study designed to identify elements of a human-robot encounter that correlate with observed stress response. Stress is a key component of perceived safety and is strongly associated with human physiological response. In this study a Boston Dynamics Spot and a Unitree Go1 navigate autonomously through a shared environment occupied by human participants wearing multimodal physiological sensors to track their electrocardiography (ECG) and electrodermal activity (EDA). The encounters are varied through several trials and participants self-rate their stress levels after each encounter. The study resulted in a multidimensional dataset archiving various objective and subjective aspects of a human-robot encounter, containing insights for understanding perceived safety in such encounters. To this end, acute stress responses were decoded from the human participants' ECG and EDA and compared across different human-robot encounter conditions. Statistical analysis of data indicate that on average (1) participants feel more stress during encounters compared to baselines, (2) participants feel more stress encountering multiple robots compared to a single robot and (3) participants stress increases during navigation behavior compared with search behavior.

Human Stress Response and Perceived Safety During Encounters with Quadruped Robots

This paper investigates patient perceptions towards the potential use of humanoid assistive robots in rehabilitation hospitals. We ran sessions with a demo of a humanoid robot performing assistive tasks for the participant. After the demo the participants were interviewed. The findings indicate an overall openness and acceptance of the concept of humanoid robots in healthcare justified both with facilitation of daily activities for the patients and the potential relief of workload for the healthcare staff. However, certain core concerns were raised regarding the suitability of humanoid assistive robots to individual patient needs and phases of recovery, as well as issues related to safety, privacy, and implementation dilemmas. This paper discusses the findings attending to literature, identified research gaps, and aims to provide recommendations for the deployment of robotics in real-world hospital settings attending to patients viewpoints.

Beyond Acceptance: Patients Perspectives on Humanoid Assistive Robots in Healthcare

Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involve objects. Existing methods still struggle to recognize unseen actions or require extensive training data. To overcome these problems, we pro- pose Dual-VCLIP, a unified approach for zero-shot multi-label action recognition. Dual-VCLIP enhances VCLIP, a zero-shot action recognition method, with the DualCoOp method for multi-label image classification. The strength of our method is that at training time it only learns two prompts, and it is therefore much simpler than other methods. We validate our method on the Charades dataset that includes a majority of object-based actions, demonstrating that  despite its simplicity  our method performs favorably with respect to existing methods on the complete dataset, and promising performance when tested on unseen actions. Our contribution emphasizes the impact of verb-object class-splits during robots training for new cooperative tasks, highlighting the influence on the performance and giving insights into mitigating biases. Dataset splits and code are publicly available on the project's repository https://github.com/hsp-iit/DualVCLIP.

The Impact of Compositionality in Zero-Shot Multi-Label Action Recognition for Object-Based Tasks

This study employs both qualitative and quantitative approaches to explore perceptions of sex robots. Utilizing grounded theory, a thematic analysis revealed three primary themes: uncanny, artificial, and curious. Following this, an exploratory factor analysis was conducted to evaluate the dimensionality of these themes. Regression analyses were then performed to examine the influence of individual differences on these dimensions. The findings indicated that females perceived sexbots as more uncanny, older individuals perceived sexbots as more artificial, Americans were more curious about sexbots than the British, and males were more curious about sexbots, particularly the female sexbots.

Uncanny, Artificial, but Curious: A Mixed Methods Study on Responses to Sex Robots

Understanding prosociality toward robots furthers our insight into robot acceptance and perception. This study (N=310) adopts a third-person perspective to investigate outcomes of the Dictator Game paradigm. Participants were tasked with evaluating the shared allocation of four distinct resources (money, electronic tools, food, and access to electricity). Results reveal that only 27% of answers indicate a preference for non-sharing. Our findings also show a tendency towards greater sharing of electronic tools and lesser sharing of food compared to access to electricity. Notably, perceptions of robot trustworthiness do not influence peoples sharing judgments, while anthropomorphic perception does play a significant role. Participants expressed greater confidence in their judgments regarding sharing food with robots, a resource where participants preferred non-sharing. This suggests that individuals most likely distinctly categorize robots when contemplating resources necessary for sustaining life. Our study indicates an emerging societal expectation regarding the types and extent of resources to be shared with robots, thus contributing to the delineation of evolving robot social norms.

Judging Sharing with Robots: A Third-Person Perspective on Prosociality

This study investigates the impact of the Internet of Robotic Things (IoRT) on user perceptions when the system encourages users behavior in accordance with social norms. Utilizing a 2 (Relationship between humans: hierarchical vs. flat) x2 (IoRT mode: hierarchical vs. flat) mixed-participants design, the research explores how different modes of IoRT operationflat and hierarchicalaffect perceived usefulness, service evaluation of the IoRT, and perceived sociability and familiarity towards counterparts. Results indicated that the flat mode was rated more favorably than the hierarchical mode in terms of usefulness and service evaluation. Additionally, sociability and familiarity ratings towards the interaction counterpart significantly varied based on the relationship type and IoRT mode, with the flat mode enhancing these perceptions particularly in flat relationship conditions.

The Effect of IoRT on Technology Perception and User Impressions through Social Norm Compliance

Endoscopic retrograde cholangiopancreatography (ERCP) is a minimally invasive technique for treating biliary and pancreatic diseases. It requires inserting a guide wire into the bile duct or pancreatic duct to enable further procedures. However, this is challenging because the duct openings are at an obtuse angle to the direction of the instrument entering the duodenum. The guide wire may fail to adjust its position and posture, causing mis-insertion and tissue damage. To address this issue, we proposed a flexible surgical robot with a concentric design that combines magnetic-driven and tendon-driven methods. The NiTi wires work as both the backbone and the driving element of the outer tube, and a flexible inner part equipped with several magnets works as a guide wire that can bend in random directions. By using two steerable segments, our robot has higher flexibility and a lower chance of tissue damage. Then, we analyse the dexterity of the proposed mechanism and performed a phantom experiment. The result shows that it can perform insertion with different postures and adapt to the natural configuration of the natural cavity of a human.

Magnetic Concentric Guidewire Inserting Robot towards Endoscopic Retrograde Cholangiopancreatography

This paper introduces an enhanced hand gesture recognition system utilizing inductive sensors, specifically developed for the identification of American Sign Language (ASL) gestures. The system is built into a regular glove with conductive threads by sewing coils onto various areas, including the fingers, wrist, palm, and ulnar regions. To increase the system's sensitivity, a data acquisition system measures a tank circuit created by each sewn coil and external components. The effectiveness of the system is rigorously evaluated on a sample of 10 participants. The identification of the 26 ASL letters is achieved using a random forest machine learning algorithm. To enhance the diversity and applicability of the machine learning model, a data augmentation scheme is utilized. The outcomes reveal a notable level of accuracy, achieving 98.91% accuracy with 5-fold cross-validation and 98.82% accuracy with leave-one-subject-out cross-validation.

Premium content

Downloads

Next from IEEE RO-MAN 2024

Personality and Memory-Based Framework for Emotionally Intelligent Agents

Similar lecture

Task-Oriented Robot-to-Human Handovers in Collaborative Tool-Use Tasks