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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&#39;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.

IEEE RO-MAN 2024

Building Intelligent and Intelligible AI: A Framework for Human-Like Autonomy and Explainability in Critical Infrastructure

creating human-robot relationships

trustworthy ai

explainability

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.

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”.

This paper investigates the effects of gender neutralization on the perception of anthropomorphism, gender specificity, and novelty for human voices, comparing spoken and hummed voice modalities. We evaluated gender-neutralized and original voice samples in both spoken and hummed formats using an online survey. Our results confirm that gender-neutralizing filters effectively reduce perceived gender specificity in both modalities, supporting their use in creating gender-neutral voices for humanoid robots. Hummed voices were perceived as more anthropomorphic and less novel than spoken voices, suggesting that non-verbal sound modalities can enhance the human likeness of gender-neutral androids while maintaining gender ambiguity. The study contributes to HRI by highlighting the potential of humming to fulfill users' expectations of interaction with android robots.

Evaluating Gender Ambiguity, Novelty and Anthropomorphism in Humming and Talking Voices for Robots

Social robots are finding their way into our society and must adapt to the social norms of everyday life. It is often unavoidable for robotic assistants to interrupt humans in their activities. These interruptions must therefore be designed in such a way that they have as little negative impact as possible on the ongoing activity and the perception of the robot. In this study, we investigate whether interruptions regarding health-promoting behavior delivered by a social robot in an office situation should be phrased politely rather than directly. A system was implemented to trigger either politely or directly phrased interruptions in a laboratory user study with participants executing an office task. We examined the perception of the robot, the task, and the interruption, as well as whether the health-promoting behavior was performed by the users. The results show that there were no significant differences in the perception of the robot and the interruptions, while the temporal demand of the task was perceived to be higher in the polite condition. In addition, the intention to follow the polite prompts was significantly higher, but there was no difference in the actual compliance with the prompts in the study itself.

Excuse Me, May I Disturb You? the Influence of Politeness of a Social Robot on the Perception of Interruptions

This paper presents the design of a novel social robot, namely Patbot, to engage people in playful interactions to help reduce their anxiety in waiting environments. We introduce the rationale and design decisions made during the development of the robot. We evaluated the robot's usability through an experiment within a simulated waiting environment, followed by interviews with the participants. The study indicated a positive effect of interacting with Patbot regarding STAI-6 anxiety measures. Other quantitative and qualitative measures, including Godspeed questionnaires, observation, and post-interviews, revealed positive impressions made by Patbot regarding its likability and animacy and that it could engage individuals in intuitive and playful activities. Our findings suggest the promise of designing social robots for entertainment and relaxation to enhance people's emotional experiences in waiting environments.

Patbot: Designing a Social Robot to Reduce Anxiety in Waiting Environments

We explore the potential of mobile motion as an expression modality for home social robots with a low expressivity. To gauge the potential of mobile motion, we examine its relative efficacy in terms of emotion perception accuracy, emotion intensity, and impression (anthropomorphism and animacy) compared to screen-based facial expressions. Additionally, we explore how users perceive the emotional intensity of expressions based on different degrees of expression by manipulating emotional features such as motion size and speed. Motion expressions are less accurate than facial expressions, but perform on par with facial expressions in other metrics. Its dynamic and expressive features elicit powerful emotional conveyance, in contrast to the low emotional impact associated with the monotonous nature of screen-based facial expressions. Further research is needed on ME for specific emotions, but in general, the higher the degree of expression, the more intense the emotion conveyed. We show that the degree of expression, i.e., the combination of emotional features and their modulation, can be utilized to express the emotional intensity, situational dependence, and personality of robots. In conclusion, we argue that mobile motion is a promising method to compensate for the weaknesses of screen-based facial expression, which is the dominant expression modality for low expressivity robots.

Exploring the Potential of Wheel-Based Mobile Motion As an Emotional Expression Modality

Integrating intelligent systems, such as robots, into dynamic group settings poses challenges due to the mutual influence of human behaviors and internal states. A robust representation of social interaction dynamics is essential for effective human-robot collaboration. Existing approaches often narrow their focus to facial expressions or speech, overlooking the broader context. We propose employing an adapted Temporal Graph Networks to comprehensively represent social interaction dynamics while enabling its practical implementation. Our method incorporates temporal multi-modal behavioral data including gaze interaction, voice activity and environmental context. This representation of social interaction dynamics is trained as a link prediction problem using annotated gaze interaction data. The F1-score outperformed the baseline model by 37.0%. This improvement is consistent for a secondary task of next speaker prediction which achieves an improvement of 29.0%. Our contributions are two-fold, including a model to representing social interaction dynamics which can be used for many downstream human-robot interaction tasks like human state inference and next speaker prediction. More importantly, this is achieved using a more concise yet efficient message-passing method, significantly reducing the message size from 768 to 14 while outperforming the baseline model.

Modeling Social Interaction Dynamics Using Temporal Graph Networks

A crucial part of traditional reinforcement learning (RL) is the initial exploration phase, in which trying available actions randomly is a critical element. As random behavior might be detrimental to a social interaction, this work proposes a novel paradigm for learning social robot behavior - the use of shielding to ensure socially appropriate behavior during exploration and learning. We explore how a data-driven approach for shielding could be used to generate listening behavior. In a video-based user study (N=110), we compare shielded exploration to two other exploration methods. We show that the shielded exploration is perceived as more comforting and appropriate than a straightforward random approach. Based on our findings, we discuss the potential for future work using shielded and socially guided approaches for learning idiosyncratic social robot behaviors through RL.

Shielding for Socially Appropriate Robot Listening Behaviors

This paper presents a system for diversity-aware autonomous conversation leveraging the capabilities of large language models (LLMs). The system adapts to diverse populations and individuals, considering factors like background, personality, age, gender, and culture. The conversation flow is guided by the structure of the system's pre-established knowledge base, while LLMs are tasked with various functions, including generating diversity-aware sentences. Achieving diversity-awareness involves providing carefully crafted prompts to the models, incorporating comprehensive information about users, conversation history, contextual details, and specific guidelines. To assess the system's performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators.

Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness

This paper presents a comprehensive framework to enhance Human-Robot Collaboration (HRC) in real-world scenarios. It introduces a formalism to model articulated tasks, requiring cooperation between two agents, through a smaller set of primitives. Our implementation leverages Hierarchical Task Networks (HTN) planning and a modular multisensory perception pipeline, which includes vision, human activity recognition, and tactile sensing. To showcase the system's scalability, we present an experimental scenario where two humans alternate in collaborating with a Baxter robot to assemble four pieces of furniture with variable components. This integration highlights promising advancements in HRC, suggesting a scalable approach for complex, cooperative tasks across diverse applications.

A Modular Framework for Flexible Planning in Human-Robot Collaboration

Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations.

How Is the Pilot Doing: VTOL Pilot Workload Estimation by Multimodal Machine Learning on Psycho-Physiological Signals

Virtual Reality (VR) environments are used as a way to let humans experiment with algorithms, techniques, and situations in various application areas, including emergency management, serious games, smart manufacturing, and precision agriculture. They are especially relevant when experiments in the real world may be harmful to human operators. As VR environments are the closest possible faithful replicas of real environments, many recent works focus on the employment of such tools as a means to generate synthetic datasets that can be used for training machine and deep learning models, especially in situations where obtaining real datasets can be difficult. In this paper, we introduce a strategy to generate a dataset for pose estimation in the challenging scenario of precision agriculture. Finally, the quality of the generated dataset was evaluated.

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Evaluating Gender Ambiguity, Novelty and Anthropomorphism in Humming and Talking Voices for Robots

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Evaluating Gender Ambiguity, Novelty and Anthropomorphism in Humming and Talking Voices for Robots

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