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This paper introduces a novel methodology for the partitioning of the social space associated with arbitrary groups of individuals into approachable regions. The proposed method exploits boundary points within pre-identified social spaces and classifies them into three distinct levels based on the field of view between pairs of individuals within the group. The effectiveness of this approach was rigorously assessed across both static and dynamic scenarios, illustrating successful segmentation and classification of approachable regions. Furthermore, the methodology underwent comprehensive testing using publicly available datasets, yielding consistently satisfactory outcomes. This innovative approach represents a substantial contribution to the field of human-robot interaction, showcasing significant potential for future enhancements and advancements.

IEEE RO-MAN 2024

Social Space Segmentation for Approaching Tasks

approachable regions

social space

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”.

Gossip is a human behavior that has been shown to strengthen bonds, trust, and the feeling of inclusion between the gossiper and the person with whom they share the gossip. As humans engage more with social robots, fostering bonds between them is critical for meaningful interactions. In this paper, we investigated how gossiping can affect the perception of group inclusion and trust between a human and a robot. In this between-subjects user study (N = 38), we compared the effects of a robot that gossips to the participant in either a positive or negative way about the experimenter during an interaction. We found that participants in the positive condition reported a significant increase in group inclusion with the robot, while participants in the negative condition did not. We also found that participants' moral trust in the negative condition significantly decreased. Our results suggested that positive gossip can be beneficial to human-robot team cohesion.

The Effects of a Gossiping Robot on Team Cohesion

Humans have the ability to anticipate what will happen in their environment based on perceived information; this ability is often manifested as an externally observable behavioral reaction. As robots become more prevalent in human spaces, robots could attend to these visible anticipatory responses to their actions to assess ``Is this a bad idea?'' In this study, we delved into the potential of human anticipatory reaction recognition to predict outcomes. We conducted a user study wherein 30 participants watched videos of action scenarios and were asked about their anticipated outcome of the situation shown in each video (``good'' or ``bad''). We collected video and audio data of the participants reactions as they were watching these videos. We then carefully analyzed the participants' behavioral anticipatory responses; this data was used to train machine learning models to predict anticipated outcomes based on human observable behavior. Reactions are multimodal, compound and diverse, and we find significant differences in facial reactions. Model performances are around 0.5-0.6 test accuracy, and increase notably when nonreactive participants are excluded from the dataset. We discuss the implications of these findings and future work. This research offers insights into improving the safety and efficiency of human-robot interactions, contributing to the evolving field of robotics and human-robot collaboration.

 “Bad Idea, Right?” Exploring Anticipatory Human Reactions for Outcome Prediction in HRI

For social robots operating in home environments, identifying appropriate parking locations which are &quot;out of the way&quot; is a challenging and multi-faceted problem. This paper proposes a solution to one core aspect of that problem, specifically a model for estimating locations where the robot may block walking paths through narrow spaces. For generality, this model assumes no <i>a priori</i> knowledge about user behaviors or semantic features in the space, and is derived purely from spatial geometry based on a standard 2D occupancy map. An experimental validation based on self-reported parking spot preferences from long-term robot users demonstrates that the proposed model captures 74% of user preferences, outperforming a naive baseline condition in selecting user-preferred parking spots. The proposed method provides a basis for estimating socially-appropriate parking locations for robots operating in the home or other unstructured social spaces and serves as a foundation for developing more sophisticated parking spot preference models in the future.

Where can I park my robot? Modeling out-of-the-way parking spots in the home using room geometry

Robot-moderated group discussions have the potential to facilitate engaging and productive interactions among human participants. Previous work on topic management in conversational agents has predominantly focused on human engagement and topic personalization, with the agent having an active role in the discussion. Also, studies have shown the usefulness of including robots in groups, yet further exploration is still needed for robots to learn when to change the topic while facilitating discussions. Accordingly, our work investigates the suitability of machine-learning models and audiovisual non-verbal features in predicting appropriate topic changes. We utilized interactions between a robot moderator and human participants, which we annotated and used for extracting acoustic and body language-related features. We provide a detailed analysis of the performance of machine learning approaches using sequential and non-sequential data with different sets of features. The results indicate promising performance in classifying inappropriate topic changes, outperforming rule-based approaches. Additionally, acoustic features exhibited comparable performance and robustness compared to the complete set of multimodal features. Our annotated data is publicly available at https://github.com/ghadj/topic-change-robot-discussions-data-2024.

Let's Move On: Topic Change in Robot-Facilitated Group Discussions

The ageing of population led to an increase of mild cognitive impairment cases, causing a struggling for society. Even though cognitive therapy is efficient in counteracting and slowing down the decline, due to the high demand for treatments, it is becoming increasingly difficult to deliver the therapy. In this scenario, the engAGE project aims to build a platform by integrating existing technologies like mobile app and social robot, to innovate cognitive therapy for older people in Europe. In this paper, the usability and the acceptability of the Pepper robot by Italian older people with mild cognitive impairment after 3 months of use in weekly group sessions are analyzed and discussed. Results show that acceptability, expressed in terms of intention to use, perceived usefulness, and positive attitude, overcomes the ease to use. Moreover, the group sessions with Pepper encourage the socialization and the participation of older people, who feel included and like protagonists of the co-design process.

A Robot to Guide Group-Therapy against Cognitive Decline: An Italian Pilot Study

The population of older adults is growing worldwide, and with it, there is a need for appropriate and effective cognitive therapies. Social robots have shown potential as therapeutic tools for older adults, providing companionship, entertainment, and therapeutic support. In particular, storytelling activities promote cognitive stimulation, socio-emotional skills, and simple entertainment. However, conventional storytelling methodologies concerning engagement, personalization, and interactivity exhibit a potential for greater diversity. These traditional approaches often require extensive preparation time, which can limit their feasibility and adaptability in diverse settings. Social robots can help overcome these limitations by providing a more interactive and engaging storytelling experience. In this paper, we present an approach to enhance the storytelling capabilities of the EBO robot, a social robot designed for interaction with older adult people. As a novelty, in addition to integrating the proposed system in the CORTEX cognitive architecture, we offer to include the therapist in the loop and integrate Artificial Intelligence techniques, including a Large Language Model, ChatGPT, to enable the robot to generate natural-sounding and engaging narratives, adjust its interactions based on the cognitive impairments, and align with the interests and preferences of the user. We demonstrate the effectiveness of our approach through a case study involving older adult participants and therapists. Our preliminary results show that the EBO robot, integrating AI techniques, can provide an interactive, customizable, and socially aware storytelling experience that promotes cognitive stimulation, socio-emotional skills, and simple entertainment that is engaging and enjoyable for

AI-Enhanced Social Robots for Older Adults Care: Evaluating the Efficacy of ChatGPT-Powered Storytelling in the EBO Platform

As autonomous functional robots proliferate in public spaces, understanding their acceptance among diverse populations becomes crucial. Traditional models of technology acceptance focus on behavioral and cognitive factors, overlooking the sociocultural and temporal scaffolding of human-robot interaction. To address this gap, we present two themes (Identity Matters, Community Matters) resulting from a qualitative analysis of ethnographic interviews (n = 20) with passersby and vendors who have experienced Starship delivery robots deployed in their communities. Situating the outcomes of our analysis in relation to existing technology acceptance models, we offer four lessons contributing to the efforts in understanding existence and usage-related acceptance of autonomous functional robots in public spaces.

Identity and Community Matter(s): Exploring Sociocultural Dimensions of Functional Service Robots Acceptance in Public Spaces

This paper introduces a lifecycle perspective on social robot design and human-robot interaction, and explores the practices of maintenance, repair, and letting go of social robots. Drawing on interviews with robot owners and representatives of robot development and repair companies, we argue that these previously disregarded aspects of everyday use provide a context for negotiating the social value and meaning of interactions with robots. We discuss owner concerns about robot obsolescence, as well as company support for long term human-robot interaction through repair, reuse, and giving owners closure in letting go of robots they can no longer use. Our work expands the purview of HRI study and design beyond the common focus on initial design and adoption and to perceptions and practices that can foster more enduring relationships with social robots, support sustainability in robot design, and address owners emotional attachment to robots.

The Lifecycle of Social Robots: Obsolescence and Values in Repair

In this work, we introduce the adaptation of ego network analysis (ENA) as a methodology to investigate group- robot interactions (GRI) in public spaces. Based on a field study with 16 GRI, our research explores the complexities of group dynamics and offers a reflection on the use of ENA to assess these interactions. Our results contribute to a nuanced understanding of how group structures influence the perception of social robots in GRI. The insights on the use of ENA in human-robot interaction (HRI) lay the foundation for strengthening methodologies and advancing our understanding of social dynamics in HRI.

Connecting the Dots: Advancing the Understanding of Group-Robot Interactions in Public Spaces Through Ego Network Analysis

This paper presents a novel AI system architecture designed for critical infrastructure, emphasizing the importance of human-robot interaction (HRI) in improving the explainability of AI decision-making. The proposed approach combines Case-Based Reasoning (CBR) and ontology to create a dynamic AI framework that ensures transparency and trustworthiness in autonomous operations. In which the Case-Based Reasoning (CBR) component employs prototype and exemplar methods from cognitive psychology to mimic human decision-making, while the ontology component organizes the knowledge base, enhancing clarity and comprehension. The proposed system architecture consists of three interconnected components that work together to improve the AI's explainability and adaptability in dynamic environments. These components include: 1) a Case-Based Reasoning (CBR)-powered decision-making module that enables the AI to learn from past experiences and justify its actions in a human-understandable format; 2) an ontology-guided knowledge framework that provides a structured and semantic representation of information to guide AI operations; and 3) an HRI mechanism that facilitates effective collaboration between humans and the AI system, ensuring that autonomous decisions are transparent and aligned with human oversight.We evaluate the proposed architecture in a simulated environment, demonstrating improved AI explainability and reliability. Our findings show that the integration of CBR and ontology links decision-making with transparency, highlighting the role of HRI in accountable AI for critical infrastructure. The paper discusses the design and outcomes of the study, paving the way for the development of more transparent and trustworthy AI systems for critical infrastructure.

Social Space Segmentation for Approaching Tasks

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Social Space Segmentation for Approaching Tasks

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The Effects of a Gossiping Robot on Team Cohesion

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