United States

HRI research has evolved to take a broader, more inclusive view of how culture influences our interaction with robots. As we delve deeper into cultural integration in HRI, it has become evident that while integrating cultural aspects offers new opportunities, it requires careful consideration due to the heightened sensitivity to the fluid nature of cultural dynamics. Our study focuses on a particular case and examines the role of context and personal preferences in a restaurant setting. We investigate how preferences for cross-cultural greetings performed by a humanoid robot can change based on the restaurant theme and describe what factors influence these preferences by looking at two different groups who participated based on different ethnic greetings. Our study reveals insight into how ethnicity, percentage of life lived in Western countries, personality variations, and implementation of cultural aspects influence the likability of robotic greeting gestures. Our investigations highlight the complexity of creating culturally adaptive robots that demonstrate the cultural norms and gestures that align with the expectations of the respective cultural groups.

IEEE RO-MAN 2024

Greeting Preferences in a Hospitality Context: A Cross-Cultural Study with a Social Robot

cultural adaptation

cross-cultural

hospitality

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

technical paper

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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!

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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”.

We investigated the impact of a government policy on Artificial Intelligence (AI) technology on people's trust in AI applications in society. Specifically, we sought to examine how trust in AI applications is affected by the government's emphasis on either innovation or regulation, and the risks associated with AI applications. In an online experiment, participants were presented with a government policy that prioritized either the innovation of AI technology for economic growth or the regulation of AI technology for the protection of public interest. Participants then indicated their trust in various AI application cases with varying levels of perceived risk. We found that perceived risk influenced participants trust in the applications, and that participants strongly supported AI policies prioritizing regulation. There were also possible trends suggesting the interactive effect of policies and perceived risk on participants trust in AI applications and the effect of policies on the decrease of participants general trust in AI technology. This work represents a crucial preliminary step in contributing to the ongoing global debate on the AI regulation dilemma.

Balancing Innovation and Regulation: The Impact of Government Policies and Perceived Risk on Public Trust in AI Applications

Social robots interacting with people in public spaces may access and collect their personal information, which raises privacy concerns regarding the disclosure of personal information. This paper aims to investigate factors impacting individuals' intention to disclose personal information to a social robot in public spaces and evaluate the actual disclosure during the interaction with the robot. For this purpose, a model is proposed to predict people's intentions to disclose information to a social robot. We conducted our experiment at a public festival with more than 100 participants using the social robot ARI. The findings reveal the substantial impact of factors including risk beliefs, trusting beliefs, perceived enjoyment, and social influence on the intention to disclose personal information. Moreover, they reveal that although only a small percentage (6.20%) of people had the intention to disclose information to the social robot, most participants (98.00%) finally disclosed their personal information.

How Do People Intend to Disclose Personal Information to a Social Robot in Public Spaces?

Social-emotional learning (SEL) skills are essential for children to develop to provide a foundation for future relational and academic success. Using art as a medium for creation or as a topic to provoke conversation is a well-known method of SEL education. Similarly, social robots have been used to teach SEL competencies like empathy, but the combination of art and social robotics has been minimally explored. In this paper, we present a novel child-robot interaction designed to foster empathy and promote SEL competencies via a conversation about art scaffolded by a social robot. Participants (N=11, age range: 7-11) conversed with a social robot about emotional and neutral art. Analysis of video and speech data demonstrated that this interaction design successfully engaged children in the practice of SEL skills, like emotion recognition and self-awareness, and greater rates of empathetic reasoning were observed when children engaged with the robot about emotional art. This study demonstrated that art-based reflection with a social robot, particularly on emotional art, can foster empathy in children, and interactions with a social robot may help alleviate discomfort when sharing deep or vulnerable emotions.

A HeARTfelt Robot: Social Robot-Driven Deep Emotional Art Reflection with Children

Congestion in urban spaces sometimes leads to serious accidents. To prevent a congestion and serious accident, it is important to properly arrange-building layouts and guide pedestrians safely based on, mathematical modeling and simulation of pedestrian flows. In the real world, however, each pedestrian's behavior is so complicated and often includes spatio-temporally multiscale decision making while most of existing studies mainly focused on a crossing area in front of a station such as Shibuya scramble crossing. In a previous report, a trained model could simulate some pedestrian trajectory, but it also generated undesirable behavior that a pedestrian ignored a traffic light. In this study, we introduce an attention mechanism to solve this problem. The performance of the model is evaluated quantitatively by the prediction error and qualitatively by visualizing the time of a traffic light switching.

Spatio-Temporal Multi-Scale Pedestrian Flow Model by Using Attention Module

Avatar robots are telepresence robots that allow people to project themselves physically in distant places. Teleoperators use such avatars to have remote social interactions. Mobility adds to their interactive capabilities. However, it also presents challenges in social spaces; for instance, teleoperators can mistakenly move their avatars in a socially inappropriate manner. Since these robots have yet to become ubiquitous, there is little real-world data to objectively analyze the presence and frequency of such undesirable movement. Consequently, the different ways such movements manifest are unknown, and their occurrence cannot be detected. To address this problem, we conducted a laboratory experiment with hired participants in a replica artwork exhibition so that we could gather realistic human-avatar interaction data using sensors. Our data analysis helped us identify three types of inadvertently inappropriate movement behavior. We present a definition of such behavior based on these three inappropriate movement types and a labeled dataset containing more than 400 interactions, 18% of which were inappropriate. We also present detection models for each inappropriate movement type trained using our dataset.

Identifying and Detecting Inadvertent Socially Inappropriate Movement of Avatar Robots

As end users interact with increasing numbers of autonomous or semi-autonomous systems, collaboration and supervision become more complicated. People must simultaneously manage multiple systems, and most current interfaces do not scale. Augmented Reality (AR) offers a promising solution by placing information over the real world, allowing users to concurrently track the scene and robot(s) -- potentially improving scaling for these devices. In this work, we leverage user gaze - a powerful indicator of attention suitable for reactive systems - to lower cognitive burden and improve performance such that scaling to multiple agents is possible. Gaze is probed in two modalities. In active mode, the user looks at a menu and presses a button to request additional information. The passive mode provides more information when the users gaze dwells on the menu. We performed two studies: 1) participants complete a visual search task with increasing numbers of virtual robotic agents and 2) participants must track the dynamic status of a team with physical agents. Results from the first study show that the passive and active interfaces provide better scaling compared to a non-interactive interface as the number of robots increases. In both studies, users preferred the passive mode, citing a lower mental demand, effort, and frustration.

Gaze-Based Augmented Reality Interfaces to Support Scalable Human-Robot Teaming

This paper introduces a voice-controlled Human Machine Interface (HMI) tailored for an assistive robotic exoskeleton glove, aimed at assisting patients coping with Brachial Plexus Injuries (BPI) in regaining their lost grasping functionality. The development of this HMI draws upon clinical experimentation results, forming a foundation for its design. The paper delves into the challenges encountered while employing a prior voice-based HMI, which necessitated an internet connection for complex computations and exhibited limitations in effectively processing concise commands. To address these issues, an innovative voice-controlled HMI system is proposed, featuring fixed-word detection to replace the speech-to-text (STT) converter and the Neutral Language Processor (NLP) to reduce computational overhead. Furthermore, the new HMI replaces the previous text-independent speaker verification with a text-dependent, one-shot learning approach. This enhancement streamlines custom retraining, significantly improving speaker verification accuracy for concise commands. Experimental results substantiate the applicability of the proposed voice-controlled HMI for assisting individuals with BPI through specialized exoskeleton gloves.

Voice-Controlled Human-Machine Interface for an Assistive Exoskeleton Glove Aiding Patients with Brachial Plexus Injuries

In this paper, we improve the robustness of a multi-camera gesture recognition system in multi-person situations by identifying and tracking the operator. This system is meant as an intelligent room to operate and interact with surrounding devices based on pointing gestures. In the method, we identify the operator by a hand-raising gesture, followed by tracking using the coordinates of the center of the operator's head and extracting only the operator's whole body. From the experimental results, we confirmed that highly accurate tracking could be performed in a multi-person situation of 2 to 5 persons, and that the success rate of extracting images of the operator's whole body was more than 70%. We also clarified issues in the operator identification process and the extraction process.

Robust Gesture-Based Appliance Control Via Operator Identification and Tracking

In recent years, labor accidents and a shortage of skilled workers due to an aging population have become significant issues at construction sites in Japan. To address these challenges, we are developing a Cyber-Physical System (CPS) platform called ROS2-TMS for Construction, which aims to improve both the efficiency and safety of earthwork operations. In this study, we propose a task management system for construction machinery using an open platform named OPERA as an additional function of ROS2-TMS for Construction. This task management system controls construction machinery using environmental information stored in a database, which collects and stores data from sensors deployed throughout the construction site, and an extended Behavior Tree. At the end of this study, the results of the initial validation tests of autonomous earthwork operations using an OPERA-compatible backhoe ZX200 are presented.

Task Management System for Construction Machinery Using the Open Platform OPERA

We propose a novel on-line multi-UAV coverage path planning method for 3D reconstruction. UAVs play ho- mogeneous roles in most existing multi-UAV path planning methods, where their flexibility is not fully exploited. The fact that UAVs can explore with different missions and strategies at different heights simultaneously is not well considered. Therefore, we give UAVs different identities by presenting a global-local pattern, where global UAV locates the interesting regions while local UAV explores these regions in succession. Leveraging the mechanism of sensorimotor coordination, our strategy adjusts the paths of local UAV on-line according to the input from global UAV in order to gather more information and save time. In addition, our method is compatible with real-time SLAM systems. Simulation shows that our strategy takes less time than other strategies based on area-division, and indicates that it gathers more information for better reconstruction.

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Balancing Innovation and Regulation: The Impact of Government Policies and Perceived Risk on Public Trust in AI Applications

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