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using a Bayesian Network to Predict user Trust in Teleoperation Robots 16th

Using a Bayesian Network to Predict User Trust in Teleope...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Cárdenas, Juan José García Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institute Polytechnique de Paris Paris France

ISBN: (纸本)9789819635245

Trust plays a crucial role in human-robot interaction (HRI), especially in teleoperation scenarios where users control robots remotely. This paper develops a static trust prediction model using a Bayesian approach to improve system design, user experience, and robotic reliability. By integrating prior knowledge with empirical data, we constructed a Bayesian model that estimates trust levels in teleoperated robotic systems. Our model incorporates physiological measures and task performance data to provide a comprehensive trust prediction framework. We evaluated the model’s performance using precision, recall, and F1 score, achieving high precision (0.92), good recall (0.84), and a balanced F1 score (0.79). These metrics demonstrate the model’s effectiveness in accurately predicting trust levels in teleoperated robotic systems. The results underscore the importance of trustworthiness in maintaining user confidence and improving system interactions. This study highlights the potential of Bayesian models in enhancing the reliability and user experience of teleoperated robots, offering valuable insights for future developments in HRI research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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PhysioFormer: A Spatio-Temporal Transformer for Physical Rehabilitation Assessment 16th

PhysioFormer: A Spatio-Temporal Transformer for Physical Re...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Marusic, Aleksa Mai Nguyen, Sao Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (纸本)9789819635245

Studies indicate that physical rehabilitation exercises recommended by healthcare professionals can enhance physical function, improve quality of life, and promote independence for physically disabled individuals. In response to the lack of immediate expert feedback on performed actions, developing an automated system for monitoring such actions and providing feedback is very much needed. In this work, we focus on skeleton-based exercise assessment, which uses skeleton data to evaluate human motion and provide a score on how well a patient performed a movement. There are several approaches to this issue, with Spatio Temporal Graph Convolutional Networks (GCN) being among the most recent. GCNs model skeleton data as graphs and utilize temporal and spatial convolutions to capture relationships between joints more effectively than previous methods. In this research, we propose a new Transformer based model, PhysioFormer. It is inspired by SkateFormer method for human action recognition, with enhanced structure to fit the task of physical rehabilitation assessment. The model leverages skeletal-temporal self-attention across different groups based on relations between joints. The evaluation is done on the KIMORE, uI-PRMD, and KERAAL datasets, benchmark datasets that provide skeleton data captured by Kinect motion system. Our model is surpassing state-of-the-art methods significantly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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Designing a Haptic Interface for Enhanced Non-Verbal Human-Robot Interaction: Integrating Heart and Lung Emotional Feedback 23

Designing a Haptic Interface for Enhanced Non-Verbal Human-R...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Saood, Adnan Liu, Yang Zhang, Heng Tapus, Adriana U2IS Ensta Paris Institut Polytechnique de Paris Paris France Ltci Laboratory Telecom Paris Institut Polytechnique de Paris Paris France Autonomous Systems and Robotics Lab U2IS Ensta Paris Institut Polytechnique de Paris France

ISBN: (纸本)9798350373578

using non-verbal cues such as breathing and heartbeat signals as media for Human-Robot Interaction (HRI) enables subtle exchanges between humans and robots beyond explicit verbal or visual cues. This study describes the development of a novel pneumatic haptic interface that replicates human breathing and heartbeat animations for a humanoid robot and investigates its application for enhancing non-verbal HRI. The experimental results validate the effectiveness of this haptic device and highlight the significance of non-verbal cues in conveying emotional states. Introducing the haptic interface as a means for delivering involuntary, non-verbal emotional signals significantly enhances human-robot interaction compared to using gestures alone. © 2024 IEEE.

关键词： Human robot interaction

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Diaphragmatic Breathing Guidance using a Humanoid Robot with a Soft Robotic Haptic Interface for Anxiety Reduction 16th

Diaphragmatic Breathing Guidance Using a Humanoid Robot wit...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Saood, Adnan Liu, Yang Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institute Polytechnique de Paris Palaiseau France Information Processing and Communications Laboratory/LTCI Telecom Paris Institute Polytechnique de Paris Palaiseau France

ISBN: (纸本)9789819635245

Anxiety disorders profoundly affect individuals’ well-being and daily lives, manifesting in both physical and emotional symptoms. Although pharmacological treatments and cognitive-behavioral therapies are commonly used, diaphragmatic breathing exercises have shown promise as a non-pharmacological intervention. This work presents an innovative device: a silicone haptic interface mounted on the chest of a humanoid robot, designed to simulate human heartbeat and breathing. This device, employing a baromorphic system design, offers a good representation of human respiration. Our study assesses the effectiveness of this haptic interface in guiding diaphragmatic breathing exercises to reduce anxiety levels. Eighteen participants underwent a standard anxiety-inducing procedure followed by a guided diaphragmatic breathing exercise with the robot. They were divided into two groups: ten with the haptic interface and eight without it. Both groups experienced similar reductions in anxiety levels. Those using the haptic interface reported the robot as more natural and supportive in post-experiment interviews. This exploratory study illustrates the potential of social robots with haptic interfaces to enhance therapeutic practices for anxiety reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropomorphic robots

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Exploring Help-Seeking Behavior, Performance, and Cognitive Load in Individual Tutoring: A Comparative Study between Human Tutors and Social Robots

Exploring Help-Seeking Behavior, Performance, and Cognitive ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Social robots have become increasingly prevalent in the context of one-on-one tutoring, serving as effective educational aids. In response to this trend, the present study was devised to conduct a comparative analysis between human tutors and robot tutors. Additionally, the study aims to investigate how varying previous knowledge in robots influence students’ tendencies for help-seeking. By examining the performance and physiological signals of participants, this research seeks to provide valuable insights into the effectiveness of social robots in educational contexts. 21 participants were divided into three groups, each seeking assistance from a human tutor (HT), seeking help from a robot without any prior knowledge of robots (RT1), and seeking help from a robot after gaining some understanding of its capabilities (RT2). Our results demonstrated that participants sought more help from robot than from human and participants in Group RT2 performed better than participants in Group RT1. However, participants experienced greater cognitive load when interacting with a robot tutor compared to interacting with a human tutor. Future work could focus on developing interventions to alleviate students’ cognitive load during interactions with robot tutors.

关键词： Social robots Robot vision systems Cognitive load Market research Cameras Physiology Behavioral sciences Robots Thermal stresses Stress

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Estimating Complexity for Perception-based ADAS in unstructured Road Environments

Estimating Complexity for Perception-based ADAS in Unstructu...

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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on united States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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Robots in education: Influence of Regulatory Focus Theory

Robots in education: Influence of Regulatory Focus Theory

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

The Covid-19 pandemic has massively developed the use of distance learning. The limits of this practice have gradually come to light, both for students and for teachers. It is now crucial to design alternative solutions to overcome the shortcomings of videoconferencing in terms of involvement, concentration, learning, and equity. Social robots are increasingly used as tutors in the educational context and help improve teaching efficiency. Many psychology-based principles have been applied in education to guide instructional strategies, motivate students, and create a positive and productive learning environment. In this work, we use Regulatory Focus Theory (RFT), which categorizes an individual’s motivation into two types: Promotion and Prevention. Promotion-focused individuals are motivated by the potential for growth and achievement, whereas prevention-focused individuals are motivated by the potential for avoiding negative outcomes. Based on RFT, we aim to explore if and how the regulatory-focused behavior of the tutor robot can affect participants’ learning outcomes. In this work, a language learning scenario was designed with two conditions: (1) a robot tutor with promotion-focused behavior, (2) a robot tutor with prevention-focused behavior. The results are encouraging and support that promotion robot tutor can increase the learning efficiency of promotion participants and prevention robot tutor will enhance the learning interest of prevention participants.

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Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Analyzing Data Efficiency and Performance of Machine Learnin...

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European Conference on Mobile Robots (ECMR)

作者： Aleksa Marusic Louis Annabi Sao Mai Nguyen Adriana Tapus Autonomous Systems and Robotics Lab Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris Palaiseau France Dep. Informatique IMT Atlantique

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. Computer-aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.

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Robot Laughter: Does an appropriate laugh facilitate the robot’s humor performance?

Robot Laughter: Does an appropriate laugh facilitate the rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Heng Zhang Xiaoxuan Hei Junpei Zhong Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Laughter serves as a subtle social signal in human interaction, playing an essential role in expressing emotions and facilitating social connections. However, laughter comes in various forms and is usually accompanied by different non-verbal expressions, such as facial expressions and gestures. These accompanying factors can significantly influence the effect of laughter in diverse contexts, thus complicating the research on laughter, especially in understanding its role in social dynamics. Consequently, endowing robots with the ability to appropriately use laughter in interactions with humans is still a big challenge. Our current study focuses on the effect of robot laughter on robot humor expression. Our objective is to investigate whether and how two factors, the type of laughter and the robot laughter gesture, impact the overall humor performance. In this study, we selected four types of laughter (sarcastic, joyful, embarrassed, and relieved laughter) from a laughter corpus based on four specific types of jokes (Affiliative, Aggressive, Self-enhancing, and Self-defeating). For each type of laughter, we designed distinct robot gestures. During the humor performance, the robot NAO delivered jokes accompanied by matching or mismatching laughter, with or without corresponding gestures. To enhance the quantity and diversity of experimental data, we conducted an online survey utilizing recordings of the robot’s humor performance. The experimental findings indicate that when the robot’s laughter matches the type of humor in the joke, participants rate the humor performance significantly higher compared to situations where there is a mismatch. Additionally, the results confirm the positive impact of robot laughter gestures on humor performance.

关键词： Surveys Recording Robots

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Designing a Haptic Interface for Enhanced Non-Verbal Human-Robot Interaction: Integrating Heart and Lung Emotional Feedback

Designing a Haptic Interface for Enhanced Non-Verbal Human-R...

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IEEE-RAS International Conference on Humanoid Robots

作者： Adnan Saood Yang Liu Heng Zhang Adriana Tapus U2IS ENSTA Paris Institut Polytechnique de Paris Paris France LTCI Laboratory Telecom Paris Institut Polytechnique de Paris Paris France Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris France

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

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