United States

The field of robotics has shown significant advances in autonomous systems, particularly in robot navigation. Since the decisions made during navigation can be difficult for human operators to understand, research aims to provide explanations that improve human-robot interaction (HRI). However, generating and designing such explanations with the intention of improving robot-related human factors is still an ongoing research challenge. This paper addresses this challenge by investigating the impact of different explanation types on a set of human factors in the context of robot navigation. For this purpose, we conducted a user study that examined the impact of six different explanation types on commonly used human factors, including trust, satisfaction, situation awareness, likeability, understandability, and perceived usefulness. Additionally, the study provides indications of their general applicability for robot navigation explanations through creation of sum ranks across the observed human factor metrics. The results show that depending on the chosen explanation type, a significant impact on the measured factors can be observed. While constraint-based explanations are generally rated highly across all factors, apologetic explanations are not perceived well across all measured human factors. Our results provide insights into the impact of explanation types used for robot navigation scenarios on robot-related human factors, and also provide practical insights for designing explanations for robot navigation scenarios.

IEEE RO-MAN 2024

Influence of Different Explanation Types on Robot-Related Human Factors in Robot Navigation Tasks

explanation type

robot navigation

explainability

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

IEEE RO-MAN 2021

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

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!

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”.

This work illustrates the testing of the Spot Robot performed at the training camp of Civil Protection and ANPAS (National Association of Public Assistance) in Foligno. The camp simulates the aftermath of an earthquake with different types of collapsed buildings. We teleoperated the quadruped Spot robot in different areas of the camp where Spot needs to address different challenges. The focus of the testing was not on the objective performance of the robot but on how the robot was subjectively perceived by rescuers of ANPAS and Civil Protection. Initially, we formulated and tested two hypotheses to check if locomotion in some areas is perceived better than in other areas and if there are perceivable differences when the robot is using different types of locomotion gaits. Then, we conducted unstructured interviews with participants who observed the robot in action to describe their rescue procedures and give us suggestions and opinions on what operations they expect the robot might perform.

Perceptions and Opinions of Rescuers about a Quadruped Robot in an Earthquake Scenario

In the pursuit of enhancing the quality of life, research in domestic robotics is rapidly evolving, offering a broad spectrum of services that not only provide practical aid but also contribute to emotional well-being and social interaction. However, existing domestic robots often lack essential features necessary for continuous interaction with humans and their surroundings, such as stability, mobility, safety, size, and physical interaction capabilities. Addressing this need, this paper introduces the development of a prototype robot tailored for harmonious coexistence with humans. This robot utilizes its own heavy equipment, such as batteries and actuators, to achieve an intrinsically balanced structure along with a comprehensive set of features optimized for seamless integration into human-centric environments. Focusing on the mechanical design of the robot, we evaluated its performance through a series of experiments, highlighting its mechanical advantages, addressing its limitations, and proposing solutions to enhance its performance for future applications.

A Study on Designing a Robot with Body Features Tailored for Coexistence with Humans in Daily Life Environments

Recently, electroencephalographic (EEG) signals have been used to design and enhance human-robot interaction (HRI). In particular, error-related potentials (ErrPs) have been leveraged since very few years. These potentials can be used to provide feedback to the robot about any mismatch between the users expectations and the robots behavior, during interaction tasks. In this process, the correct classification of ErrPs is crucial, which, in turn, relies on the reliability of the process for extraction and selection of signal features. In this work, we consider an extensive list of possible features and perform a statistical analysis to assess their discriminative power for ErrP analysis. The aim is to reduce the number of features used for classification while retaining the most relevant ones only. Overall, the outcome of our study shows that some parameters have relevant importance compared to others, (i.e. temporal features, frequency features, signal processing features, and some wavelet transform coefficients), while some of the features used in existing works are not useful since they have low discriminative power.

Exploring the Most Significant Features for EEG ErrP Detection through Statistical Analysis

This article investigates mixed reality (MR) to enhance human-robot collaboration (HRC). The proposed solution adopts MR as a communication layer to convey a mobile manipulator's intentions and upcoming actions to the humans with whom it interacts, thus improving their collaboration. A user study involving 20 volunteers demonstrated the effectiveness of this MR-focused approach in facilitating collaborative tasks, with a positive effect on overall collaboration performances and human satisfaction.

Investigating Mixed Reality for Communication between Humans and Mobile Manipulators

The growing implementation of robots in societal contexts necessitates a deeper exploration of the dynamics of trust between humans and robots. This exploration should expand beyond traditional viewpoints that primarily emphasize the influence of robot performance. In the burgeoning area of social robotics, fine-tuning a robot's personality traits is increasingly recognized as a crucial element in shaping users' experiences during human-robot interaction (HRI). Research in this field has led to the creation of trust scales that encompass various trust dimensions in HRI. These scales include aspects related to performance as well as moral dimensions. Our previous study revealed that breaches of moral trust by robots impact human trust more negatively than performance trust breaches, and humans take retaliatory approaches in response to morality breaches by robots. In the present study, our main aim was to explore if trust loss and retaliation tendencies differ based on the identity of the teammates following the violations of these different trust aspects. Through multiple versions of an online search task, we examined our research questions and found that breaches of morality by robotic teammates cause a significantly higher trust loss in humans compared to human teammates. These findings highlight the importance of a robot's morality in determining how humans view a robot's trustworthiness. For effective robot design, robots must meet ethical and moral standards, which are higher than the ethical and moral standards expected from humans.

Do Humans Have Different Expectations Regarding Humans and Robots' Morality?

Autistic individuals, or individuals diagnosed with autism spectrum disorder (ASD), often experience challenges in social relations and may experience increased stress or anxiety. Recent studies highlight the potential of socially assistive robots (SARs) as tools for robot-assisted interventions. However, current human-robot interaction (HRI) designs lack consideration for the diverse anxiety issues in individuals with ASD and the need for personalized, empathetic assistance. The question that persists is whether the most recent individualized and modular intervention program for autism can also be effectively implemented through the use of SARs. This study presents a novel HRI framework that incorporates psychological factors into a modular intervention aimed at reducing users anxiety levels. The developed system integrates techniques such as human pose estimation and motion imitation while also leveraging conversational agents. During the user study, users engaged in interactions with a humanoid robot equipped with multiple intervention modules, and their anxiety and perceptions of the robots empathy were assessed through questionnaires in a within-participants study design. Results show the implemented system effectively mitigated users anxiety by assessing the differences between pre-session and post-session scores. Additionally, our findings suggest that the observed change in empathetic scores may be a contributing factor to the reduction in anxiety levels. The study offers significant insights into the use of SARs for mental health support in autistic adolescents.

An Empathetic Social Robot with Modular Anxiety Interventions for Autistic Adolescents

The deployment of social robots in public spaces has received increased interest over the past years. To execute various tasks, these robots need to process a wide array of personal data to offer services that are tailored to users requirements. While much research has been carried out regarding the creation of explainable content, little research has dealt with how data transparency - as a way to address uncertainty and concerns regarding the handling of personal data - is conveyed towards users. To examine the impact on trust, performance, and robot evaluation of modalities of transparency declarations, we conducted a virtual reality (VR) supported laboratory experiment with N = 53 participants who interacted with a robot in a public setting (a library). The interaction between users and robots is accompanied by information on the handling of users personal data using three different modalities (via posters, the robots tablet, or verbally). The results imply that, while all modalities are understandable and perceived as useful, there is no preference for any modality. This research contributes to HRI research by examining different modalities for transparency declarations in an effort to foster understandable and transparent processing of data.

Towards Understandable Transparency in Human-Robot-Interactions in Public Spaces

Human-Autonomy Teaming (HAT) is a multi-disciplinary domain with a diverse set of research needs and goals stemming from fields such as computer science, robotics, and human factors. This melting pot of fields generates a unique challenge in that there exist many disjoint research methods (measures and tasks) that cause issues with knowledge transfer and comparison between researchers. One way to address this issue is by providing researchers with a testbed containing a standardized suite of analysis tools and tasks that allow direct comparison between different approaches. Therefore, this study attempts to bring the HAT community together in a collaborative discussion to collect and organize their research needs for the future development of these testbeds. Specifically, through thematic analysis, our work reveals three emergent prongs that underpin testbed needs of HAT experts: task, AI, and technical requirements. Also, we organize our thematic analysis by priority to suggest possible paths for HAT testbed development to maximize its immediate and continued utility. Our research indicates that the HAT community places significant importance on both the pre-established, standardized functions available within the testbed and the freedom to tailor and develop their unique tasks or AI solutions.

Research Needs in Human-Autonomy Teaming: Thematic Analysis of Priority Features for Testbed Development

The efficiency of collaborative mobile robot applications is influenced by the inherent uncertainty introduced by humans' presence and active participation. This uncertainty stems from the dynamic nature of the working environment, various external factors, and human performance variability. The observed makespan of an executed plan will deviate from any deterministic estimate. This raises questions about whether a calculated plan is optimal given uncertainties, potentially risking failure to complete the plan within the estimated timeframe. This research addresses a collaborative task planning problem for a mobile robot serving multiple humans through tasks such as providing parts and fetching assemblies. To account for uncertainties in the durations needed for a single robot and multiple humans to perform different tasks, a probabilistic modeling approach is employed, treating task durations as random variables. The developed task planning algorithm considers the modeled uncertainties while searching for the most efficient plans. The outcome is a set of the best plans, where no plan is better than the other in terms of stochastic dominance. Our proposed methodology offers a systematic framework for making informed decisions regarding selecting a plan from this set, considering the desired risk level specific to the given operational context.

Risk-Aware Planning of Collaborative Mobile Robot Applications with Uncertain Task Durations

Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple heterogeneous agents which are characterized by different capabilities and/or embodiment. In this work, we propose a method to realize visual action planning in multi-agent settings by exploiting a roadmap built in a low-dimensional structured latent space and used for planning. To enable multi-agent settings, we infer possible parallel actions from a dataset composed of tuples associated with individual actions. Next, we evaluate feasibility and cost of them based on the capabilities of the multi-agent system and endow the roadmap with this information, building a capability latent space roadmap (C-LSR). Additionally, a capability suggestion strategy is designed to inform the human operator about possible missing capabilities when no paths are found. The approach is validated in a simulated burger cooking task and a real-world box packing task.

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Perceptions and Opinions of Rescuers about a Quadruped Robot in an Earthquake Scenario

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