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HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies feasibility and effectiveness. We propose VID2REAL HRI, a research framework to maximize real-world insights offered by video-based studies. The VID2REAL HRI framework was used to design an online study using first-person videos of robots as real-world encounter surrogates. The online study (n = 385) distinguished the within-subjects effects of four robot behavioral conditions on perceived social intelligence and human willingness to help the robot enter an exterior door. A real-world, between-subjects replication (n = 26) using two conditions confirmed the validity of the online studys findings and the sufficiency of the participant recruitment target (22) based on a power analysis of online study results. The VID2REAL HRI framework offers HRI researchers a principled way to take advantage of the efficiency of video-based study modalities while generating directly transferable knowledge of real-world HRI. Code and data from the study are provided at vid2real.github.io/vid2realHRI.

IEEE RO-MAN 2024

Vid2RealHRI: Align Video-Based HRI Study Designs with Real-World Settings

robot autonomy

video study

hri framework

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!

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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”.

In Japan, home robots have become popular and are widely loved. In particular, Sonys aibo users have formed a big community, and a society where humans and robots live in harmony is becoming a reality. In order for robots to be more accepted in the society, exploratory analysis and explicit description of their bonds are important. However, the scale for measuring relationships with robots has not yet been defined, so a suitable measure was created from the Friendship Function Scale to measure the relationship of friends and the Pet Awareness Scale to measure the attitude toward pets. Using the proposed scale, we investigated the role of home robots, subjective intimacy, and attitudes toward robots among 54 robot owners. The survey found that more than half of them considered robots to be pets or children. In addition, the average value of subjective intimacy was very high to indicate a very high level of attachment to the robot. Furthermore, as a result of factor analysis of the scale items, five factors were extracted: Comfort, Irreplaceability, Trust from the robot, Trust to the robot, and Social life. At this time, there was no bias in how users felt about comfort and irreplaceability, but there was a bias in how users felt about trust. Additionally, those with the highest level of subjective intimacy felt significantly more trusted by the robot than those with the other level of intimacy, suggesting that recognition of trust from the robot may be an important bond-forming strategy. In the future, we will aim to form further bonds by investigating relationships with robots that play various roles, as well as in regions other than Japan.

An Exploratory Analysis of the Harmonious Bond between Home Robots and Their Owners in Japan

COVID-19 and technological developments have increased opportunities for remote communication. This has led to a focus on ``telepresence avatar robots,'' a technology that makes people feel as if they are sharing the same place, even from remote locations. The telepresence avatar robot takes the approach of supporting communication by generating a realistic sense of conversation that incorporates technologies such as camera motion and eye contact with the speaker. However, this approach does not enable smooth communication because the robot operator cannot recognize the intention of the local worker's behavior, since the information transmitted from the remote location is less than that available in face-to-face communication. In this study, we focus on gaze-following motion as an unconscious human behavior and implement it in a robot to verify whether a robot operator can perceive the intention of a remote worker through this autonomous motion. Eye-tracking is an action in which one tracks the other's gaze in a local conversation, and it is performed unconsciously. The experimental results suggest that this autonomous behavior can generate an estimation of gazing intention&#65294; Consequently, this approach may be a useful method for achieving smoother communication.

Automatic Joint Gaze Generation between Local and Remote Persons through Telepresence Robot

To facilitate the integration of autonomous mobile robots into environments shared with humans, ensuring the comfort of pedestrians and observers (individuals observing the interaction from a third-party perspective) is crucial. This study explores the correlation between pedestrian and observer comfort and the robot's distance and pose angle during pedestrian encounters. The findings indicate that when a robot passes a pedestrian after completing its avoidance motion, it enhances the comfort of both pedestrians and observers. In addition, when a robot maintains a large distance from pedestrians while passing by, it improves pedestrian comfort. Furthermore, designing the robot's pose during passing so that it appears to move away from the pedestrian from the observer's perspective further increases the observer's comfort.

The Effect of Robot Pose and Distance on Pedestrian and Observer Comfort During Passing

Spoken language interaction is at the heart of interpersonal communication, and people flexibly adapt their speech to different individuals and environments. It is surprising that robots, and by extension other digital devices, are not equipped to adapt their speech and instead rely on fixed speech parameters, which often hinder comprehension by the user. We conducted a speech comprehension study involving 39 participants who were exposed to different environmental and contextual conditions. During the experiment, the robot articulated words using different vocal parameters, and the participants were tasked with both recognising the spoken words and rating their subjective impression of the robot's speech. The experiment's primary outcome shows that spaces with good acoustic quality positively correlate with intelligibility and user experience. However, increasing the distance between the user and the robot exacerbated the user experience, while distracting background sounds significantly reduced speech recognition accuracy and user satisfaction. We next built an adaptive voice for the robot. For this, the robot needs to know how difficult it is for a user to understand spoken language in a particular setting. We present a prediction model that rates how annoying the ambient acoustic environment is and, consequentially, how hard it is to understand someone in this setting. Then, we develop a convolutional neural network model to adapt the robot's speech parameters to different users and spaces, while taking into account the influence of ambient acoustics on intelligibility. Finally, we present an evaluation with 27 users, demonstrating superior intelligibility and user experience with adaptive voice parameters compared to fixed voice.

No More Mumbles: Enhancing Robot Intelligibility through Speech Adaptation

While extant research on human-robot-interactions (HRI) has dealt with the examination of different human characteristics, quantifying and describing the various char- acteristics of human diversity remains a challenge for HRI research. This in turn often leads to a disregard of human diversity in the design of HRI. Addressing this issue, we conducted a systematic synthesis of existing models on human diversity, culminating in the development of a model we coined the Human Diversity Wheel for Robotic Interactions (HDWRI). The goal of this model is to provide designers and researchers in HRI with an analytical lens to ensure their work considers different human characteristics. To achieve this, we started to conduct expert interviews with HRI researchers to put our model into a practical context. This paper presents the development of our model, preliminary findings of our first interviews, and an outline of future steps.

Towards a Human Diversity Wheel for Human Robot Interaction

To evaluate how explanations affect the users' understanding of robots, researchers typically elicit the user's Mental Model (MM) of the robot and then compare it to the robot's actual decision-making and behaviour. However, the user's self-rating of their level of understanding, which we define as ``user-perceived understanding'', is generally not evaluated. Moreover, this evaluation is typically done only once, while robots are often expected to interact with the same users over long periods. In this work, we suggest a framework to analyse the evolution of the mental models over time across the dimensions of completeness and correctness. We argue that the goal of explainability should be two-fold. On one hand, it should help align the user's perceived understanding with the real one. On the other hand, explainability should enhance the completeness of the mental model to a target level, which varies depending on the user type, while also striving for maximum correctness.

Evaluating the Impact of Explainability on the Users' Mental Models of Robots Over Time

With the growing use of mobile robots in urban settings, their interaction with people in shared spaces is increasing. It's crucial for these robots to navigate in a socially acceptable manner and interact properly with their environment. AI guides robot behavior by interpreting the environment and controlling actions. Poor explainability of robot decisions can lead to mental and physical risks. Proper robot-to-human interfaces can help mitigate these risks. This paper introduces systems and methods for self-explaining mobile robots in common areas using projected information. We analyze data on people's reactions to the robot, with and without self-explainable features, across various university campus settings like maker-spaces, hallways, cafeterias, and study areas. Initial analysis shows improved acceptance of the robot based on task performance and user questionnaires.

Explainable Autonomous Mobile Robots: Interface and Socially Aware Learning

This paper presents a comprehensive analysis of the contact force profile of a single-cell bidirectional soft pneumatic actuator, specifically designed to aid in the abduction and adduction of the shoulder for pediatric exosuits. The actuator was embedded in an infant-scale test rig featuring two degrees of freedom: an actuated revolute joint supporting shoulder abduction/adduction and a passive (but lockable) revolute joint supporting elbow flexion/extension. Integrated load cells and an encoder within the rig were used to measure the force applied by the actuator and the shoulder joint angle, respectively. The actuator's performance was evaluated under various anchoring points and elbow joint angles. Experimental results demonstrate that optimal performance, characterized by maximum range of motion and minimal force applied on the torso and upper arm, can be achieved when the actuator is anchored at two-thirds the length of the upper arm, with the elbow joint positioned at a 90-degree angle. The force versus pressure and joint angle graphs reveal nonlinear and hysteresis behaviors. The findings of this study yield insights about optimal anchoring points and elbow angles to minimize exerted forces without reducing the range of motion.

Force Profiling of a Shoulder Bidirectional Fabric-based Pneumatic Actuator for a Pediatric Exosuit

This paper introduces eiIBM_Robot, an emotionally supportive robot designed to deliver interpretation bias modification (IBM) exercises to individuals with depressive symptoms. The robot incorporates a conversational agent utilizing large language models and a user-centered architecture to enhance user engagement and experience. A preliminary user study (N = 38) revealed positive perceptions of the robot's performance and the effectiveness of the IBM exercises in promoting positive thinking patterns and emotional well-being. The findings highlight the potential of emotionally supportive robots to provide accessible mental health support and the importance of carefully balancing the robot's social presence and perceived role in the intervention.

EiIBM_Robot: An Emotionally Supportive Robot for Delivering Interpretation Bias Modification Exercises

Effective communication is essential in collaborative tasks, so AI-equipped robots working alongside humans need to be able to explain their behaviour in order to cooperate effectively and earn trust. We analyse and classify communications among human participants collaborating to complete a simulated emergency response task. The analysis identifies messages that relate to various kinds of interactive explanation identified in the explainable AI literature. This allows us to understand what type of explanations humans expect from their teammates in such settings, and thus where AI-equipped robots most need explanation capabilities. We find that most explanation-related messages seek clarification in the decisions or actions taken. We also confirm that messages have an impact on the performance of our simulated task.

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Vid2RealHRI: Align Video-Based HRI Study Designs with Real-World Settings

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