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Despite the rise of mobile robot deployments in home and work settings, perceived safety of users and bystanders is understudied in the human-robot interaction (HRI) literature. To address this, we present a study designed to identify elements of a human-robot encounter that correlate with observed stress response. Stress is a key component of perceived safety and is strongly associated with human physiological response. In this study a Boston Dynamics Spot and a Unitree Go1 navigate autonomously through a shared environment occupied by human participants wearing multimodal physiological sensors to track their electrocardiography (ECG) and electrodermal activity (EDA). The encounters are varied through several trials and participants self-rate their stress levels after each encounter. The study resulted in a multidimensional dataset archiving various objective and subjective aspects of a human-robot encounter, containing insights for understanding perceived safety in such encounters. To this end, acute stress responses were decoded from the human participants&#39; ECG and EDA and compared across different human-robot encounter conditions. Statistical analysis of data indicate that on average (1) participants feel more stress during encounters compared to baselines, (2) participants feel more stress encountering multiple robots compared to a single robot and (3) participants stress increases during navigation behavior compared with search behavior.

IEEE RO-MAN 2024

Human Stress Response and Perceived Safety During Encounters with Quadruped Robots

monitoring of behaviour and internal states of humans; community embedded robotics; perceived safety

Despite the rise of mobile robot deployments in home and work settings, perceived safety of users and bystanders is understudied in the human-robot interaction (HRI) literature. To address this, we present a study designed to identify elements of a human-robot encounter that correlate with observed stress response. Stress is a key component of perceived safety and is strongly associated with human physiological response. In this study a Boston Dynamics Spot and a Unitree Go1 navigate autonomously through a shared environment occupied by human participants wearing multimodal physiological sensors to track their electrocardiography (ECG) and electrodermal activity (EDA). The encounters are varied through several trials and participants self-rate their stress levels after each encounter. The study resulted in a multidimensional dataset archiving various objective and subjective aspects of a human-robot encounter, containing insights for understanding perceived safety in such encounters. To this end, acute stress responses were decoded from the human participants' ECG and EDA and compared across different human-robot encounter conditions. Statistical analysis of data indicate that on average (1) participants feel more stress during encounters compared to baselines, (2) participants feel more stress encountering multiple robots compared to a single robot and (3) participants stress increases during navigation behavior compared with search behavior.

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 patient perceptions towards the potential use of humanoid assistive robots in rehabilitation hospitals. We ran sessions with a demo of a humanoid robot performing assistive tasks for the participant. After the demo the participants were interviewed. The findings indicate an overall openness and acceptance of the concept of humanoid robots in healthcare justified both with facilitation of daily activities for the patients and the potential relief of workload for the healthcare staff. However, certain core concerns were raised regarding the suitability of humanoid assistive robots to individual patient needs and phases of recovery, as well as issues related to safety, privacy, and implementation dilemmas. This paper discusses the findings attending to literature, identified research gaps, and aims to provide recommendations for the deployment of robotics in real-world hospital settings attending to patients viewpoints.

Beyond Acceptance: Patients Perspectives on Humanoid Assistive Robots in Healthcare

Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involve objects. Existing methods still struggle to recognize unseen actions or require extensive training data. To overcome these problems, we pro- pose Dual-VCLIP, a unified approach for zero-shot multi-label action recognition. Dual-VCLIP enhances VCLIP, a zero-shot action recognition method, with the DualCoOp method for multi-label image classification. The strength of our method is that at training time it only learns two prompts, and it is therefore much simpler than other methods. We validate our method on the Charades dataset that includes a majority of object-based actions, demonstrating that  despite its simplicity  our method performs favorably with respect to existing methods on the complete dataset, and promising performance when tested on unseen actions. Our contribution emphasizes the impact of verb-object class-splits during robots training for new cooperative tasks, highlighting the influence on the performance and giving insights into mitigating biases. Dataset splits and code are publicly available on the project's repository https://github.com/hsp-iit/DualVCLIP.

The Impact of Compositionality in Zero-Shot Multi-Label Action Recognition for Object-Based Tasks

Multiple modalities can boost accuracy in the difficult task of Sign Language Recognition (SLR), however, each modality does not necessarily contribute the same quality of information. Current multi-modal approaches assign the same importance weightings to each modality, or set weightings based on unproven heuristics. This paper takes a systematic approach to find the optimal weights by performing grid search. Firstly, we create a multi-modal version of the RGB only WLASL100 data with additional hand crop and skeletal pose modalities. Secondly, we create a 3D CNN based weighted multi-modal sign language network (WMSLRnet). Finally, we run various grid searches to find the optimal weightings for each modality. We show that very minor adjustments in the weightings can have major effects on the final SLR accuracy. On WLASL100, we significantly outperform previous networks of similar design, and achieve high accuracy in SLR without highly complex pre-training schemes or extra data.

Weighted Multi-Modal Sign Language Recognition

Parent-Child Interaction Therapy (PCIT) is a well established method used by psychologists to improve and to maintain a healthy and good parent-child relationship. PCIT requires trained and certified therapists, and the therapy sessions are conducted in a special room, but participants can also practice and later perform sessions in their homes with our without therapist supervision. In order to facilitate the therapy for the parents, here we present a possible approach to introduce social robotics into the PCIT therapy. We developed a behavior model to control a mobile robot and interact with both the parent and the child during a therapy session with the goal of stimulating interaction and communication between the parent and child. The behavior of this social robot depends on the contents of the conversation between the participants, which is analyzed in real-time. For speech analysis we have developed a framework for integrating various already available components to achieve real-time Speech-to-Text transcription, and also developed our own Natural Language Understanding (NLU) engine. The evaluation of the conversation is performed by this NLU engine, which was inspired by the Dyadic Parent- Child Interaction Coding System (DPICS) used in the PCIT therapy. Depending on the evaluation results, our mobile robot can exhibit different behaviors, which were inspired by Etho-Robotics, such as approaching, getting attention, playing fetch with a toy. Besides constructing a functional real-time human conversation analysis system and a behavior model, we also performed preliminary HRI experiments with a real mobile robot controlled by the proposed model in real-time.

Towards an Interaction Stimulator Social Robot in the Parent-Child Interaction Therapy Based on Real-Time Speech Processing

Research has shown the potential of social robots to support learning in science, technology, and language. We contribute to this field by exploring how robots can support music learning. We report on a within-subjects experiment where 50 young learners practiced the piano in the presence of a robot assuming a non-evaluative and a self-assessment enhancing role implemented in a Wizard-of-Oz fashion. We examined whether the robot can make piano practice more fun, and whether initiating self-assessment to support self-regulated learning is a useful strategy for the robot. We collected quantitative self-report data to assess fun, learning, interest, engagement, and effort. We found a direct positive effect of fun on learning in the context of musical instrument practice. Path modeling showed a positive influence of having fun on learners' attitudes, interests, and learning outcomes in music education, particularly with the self- assessment robot role exhibiting superiority.

Can Robots Enhance the Learning Experience by Making Music More Fun?

This study employs both qualitative and quantitative approaches to explore perceptions of sex robots. Utilizing grounded theory, a thematic analysis revealed three primary themes: uncanny, artificial, and curious. Following this, an exploratory factor analysis was conducted to evaluate the dimensionality of these themes. Regression analyses were then performed to examine the influence of individual differences on these dimensions. The findings indicated that females perceived sexbots as more uncanny, older individuals perceived sexbots as more artificial, Americans were more curious about sexbots than the British, and males were more curious about sexbots, particularly the female sexbots.

Uncanny, Artificial, but Curious: A Mixed Methods Study on Responses to Sex Robots

Understanding prosociality toward robots furthers our insight into robot acceptance and perception. This study (N=310) adopts a third-person perspective to investigate outcomes of the Dictator Game paradigm. Participants were tasked with evaluating the shared allocation of four distinct resources (money, electronic tools, food, and access to electricity). Results reveal that only 27% of answers indicate a preference for non-sharing. Our findings also show a tendency towards greater sharing of electronic tools and lesser sharing of food compared to access to electricity. Notably, perceptions of robot trustworthiness do not influence peoples sharing judgments, while anthropomorphic perception does play a significant role. Participants expressed greater confidence in their judgments regarding sharing food with robots, a resource where participants preferred non-sharing. This suggests that individuals most likely distinctly categorize robots when contemplating resources necessary for sustaining life. Our study indicates an emerging societal expectation regarding the types and extent of resources to be shared with robots, thus contributing to the delineation of evolving robot social norms.

Judging Sharing with Robots: A Third-Person Perspective on Prosociality

This study investigates the impact of the Internet of Robotic Things (IoRT) on user perceptions when the system encourages users behavior in accordance with social norms. Utilizing a 2 (Relationship between humans: hierarchical vs. flat) x2 (IoRT mode: hierarchical vs. flat) mixed-participants design, the research explores how different modes of IoRT operationflat and hierarchicalaffect perceived usefulness, service evaluation of the IoRT, and perceived sociability and familiarity towards counterparts. Results indicated that the flat mode was rated more favorably than the hierarchical mode in terms of usefulness and service evaluation. Additionally, sociability and familiarity ratings towards the interaction counterpart significantly varied based on the relationship type and IoRT mode, with the flat mode enhancing these perceptions particularly in flat relationship conditions.

The Effect of IoRT on Technology Perception and User Impressions through Social Norm Compliance

Endoscopic retrograde cholangiopancreatography (ERCP) is a minimally invasive technique for treating biliary and pancreatic diseases. It requires inserting a guide wire into the bile duct or pancreatic duct to enable further procedures. However, this is challenging because the duct openings are at an obtuse angle to the direction of the instrument entering the duodenum. The guide wire may fail to adjust its position and posture, causing mis-insertion and tissue damage. To address this issue, we proposed a flexible surgical robot with a concentric design that combines magnetic-driven and tendon-driven methods. The NiTi wires work as both the backbone and the driving element of the outer tube, and a flexible inner part equipped with several magnets works as a guide wire that can bend in random directions. By using two steerable segments, our robot has higher flexibility and a lower chance of tissue damage. Then, we analyse the dexterity of the proposed mechanism and performed a phantom experiment. The result shows that it can perform insertion with different postures and adapt to the natural configuration of the natural cavity of a human.

Magnetic Concentric Guidewire Inserting Robot towards Endoscopic Retrograde Cholangiopancreatography

This paper introduces an enhanced hand gesture recognition system utilizing inductive sensors, specifically developed for the identification of American Sign Language (ASL) gestures. The system is built into a regular glove with conductive threads by sewing coils onto various areas, including the fingers, wrist, palm, and ulnar regions. To increase the system's sensitivity, a data acquisition system measures a tank circuit created by each sewn coil and external components. The effectiveness of the system is rigorously evaluated on a sample of 10 participants. The identification of the 26 ASL letters is achieved using a random forest machine learning algorithm. To enhance the diversity and applicability of the machine learning model, a data augmentation scheme is utilized. The outcomes reveal a notable level of accuracy, achieving 98.91% accuracy with 5-fold cross-validation and 98.82% accuracy with leave-one-subject-out cross-validation.

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Beyond Acceptance: Patients Perspectives on Humanoid Assistive Robots in Healthcare

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