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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mohamad Shaaban Simone Macciò Alessandro Carfì Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) The EngineRoom lab University of Genoa Genoa Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

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 participants demonstrated the effectiveness of this MR-focused approach in facilitating collaborative tasks, with a positive effect on overall collaboration performances and human satisfaction.

关键词： Software architecture Navigation Collaboration Mixed reality Human-robot interaction Virtual reality Computer architecture Manipulators

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhancing Hand Movement Recognition: A Hybrid Fuzzy Deep Neural Network Approach with Time-Frequency EMG Representations

Enhancing Hand Movement Recognition: A Hybrid Fuzzy Deep Neu...

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Applied Research in Electrical engineering (AREE), National and International Conference on

作者： Peyman Afshari Bijarbaneh Fatemeh Noori Shima Faramarzi Seyed Amirhossein Mousavi Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) Genoa Italy Department of Biomedical Engineering Azad University of Mashhad Mashhad Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

This study proposes a novel Hybrid Fuzzy Deep Neural Network (HFDNN) for recognizing complex hand movements using electromyography (EMG) signals. The system integrates fuzzy logic for managing uncertainty with deep neural networks (DNNs) for learning spatial and temporal features of EMG signals. Six hand movements, including lifting heavy and light objects, grasping pen-like and card-like objects, making a fist, and opening the hand, were analyzed. EMG signals were preprocessed using a band-pass filter and normalized, followed by feature extraction through short-time Fourier transform (STFT) to generate time-frequency representations as input to the HFDNN. The proposed model achieved a classification accuracy of $\mathbf{9 6. 8 \%}$, with precision, recall, and F1-scores averaging $95.7 \%, 97.2 \%$, and $96.3 \%$, respectively. Compared to traditional CNN, RNN, and hybrid CNN-RNN models, the HFDNN demonstrated superior performance, validated through statistical significance testing ($\mathbf{p}\lt 0.01$). The inclusion of fuzzy rules enhanced decision-making, particularly for overlapping classes. This work underscores the efficacy of hybrid approaches in capturing the intricacies of hand movement signals, paving the way for advancements in prosthetics control and rehabilitation. Comparative analysis with recent studies highlights the proposed method’s robustness and potential for real-world applications.

关键词： Hands Fuzzy logic Time-frequency analysis Accuracy Uncertainty Grasping Electromyography Robustness Prosthetics Testing

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Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

Computational Tradeoff in Minimum Obstacle Displacement Plan...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Antony Thomas Giulio Ferro Fulvio Mastrogiovanni Michela Robba Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grows exponentially in the size of number of movable obstacles. This work looks into approximate solutions that are computationally less intensive and differ from the optimal solution by a factor of the optimal cost.

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Scanning the Issue

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IEEE Transactions on Intelligent Transportation systems 2024年第7期25卷 6251-6275页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

The authors provide a meta-review across nearly 100 extant surveys on unmanned aerial vehicles (UAVs) applications and overview papers, extract their key messages, and investigate the extent of being complementary. Based on the findings of their survey analysis, they developed the so-called AERIAL framework, which aggregates the major challenges on the way to a successful application of UAVs for logistics, mobility, and monitoring. The framework covers the following aspects: 1) acceptance and heterogeneities; 2) economy and demand; 3) reliability and risks; 4) infrastructure and optimization; 5) automation and control; and 6) liability and standardization. They believe that their AERIAL framework and meta-review contribute towards a clearer understanding of the scientific UAV landscape challenges and the identification of potential directions for future research studies.

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IMMERSE: IMMersive Environment for Representing Self-Avatar Easily

IMMERSE: IMMersive Environment for Representing Self-Avatar ...

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IEEE Games Media Entertainment (GEM)

作者： Eros Viola Matteo Martini Fabio Solari Manuela Chessa Dept. of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Italy

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

Recently, the importance of having a representation of the users inside Virtual Reality (VR) by using avatars, which replicate the movements of real humans, has grown in several fields of application. Nowadays, developers can use many off-the-shelf devices for visualizations and tracking. Game engines like Unity3D and Unreal allow to easily integrate packages to manage head-mounted displays and other tracking devices. However, it is still difficult to combine different solutions and achieve a full representation of the human body, e.g., combining full-body and hand tracking or switching among different tracking modalities in an easy way. This paper describes IMMERSE, an open-source framework based on Unity3D, the XR Interaction toolkit, and an inverse kinematics solver. IMMERSE allows developers to insert a VR avatar in a virtual environment, animate it through different sources of 6DOF tracking measurements, which are also interchangeable during the simulation, and finally record the movements to animate other avatars or input to motion analysis techniques.

关键词： Visualization Tracking Avatars Virtual environments Entertainment industry Kinematics Switches

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A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

A Traffic Model Integrating Long Short-Term Memory Networks ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： K. Binjaku C. Pasquale S. Sacone E. K. Meçe Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Accurate traffic state estimation is crucial for effective transportation management and urban planning. In recent years, deep learning techniques, particularly Long Short-Term Memory (LSTMs) networks, have shown promise in capturing the complex temporal dynamics of traffic systems. This paper proposes a novel approach by integrating LSTM networks with the multi-class METANET model, considering on/off ramps, to enhance traffic state prediction accuracy. We develop both univariate and multivariate versions of the physics-informed multi-class LSTM model, enabling comprehensive exploration of traffic variables such as speed and flow for passenger and heavy vehicles. Experimental results demonstrate the superior predictive capabilities of our approach compared to traditional LSTM models, particularly in capturing the intricate relationship between speed and flow variables. Our findings underscore the significant benefits of incorporating physics-based principles into deep learning architectures for traffic prediction, facilitating more robust and reliable traffic state estimations for real-world applications.

关键词： Accuracy Urban planning Transportation Predictive models Reliability engineering Mathematical models Robustness State estimation Vehicle dynamics Long short term memory

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Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates

arXiv

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arXiv 2024年

作者： Angioni, Daniele Demetrio, Luca Pintor, Maura Oneto, Luca Anguita, Davide Biggio, Battista Roli, Fabio Department of Electrical and Electronic Engineering University of Cagliari Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

Machine-learning models demand for periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly-updated model may commit mistakes that the previous model did not make. Such misclassifications are referred to as negative flips, and experienced by users as a regression of performance. In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices. In particular, when updating a model to improve its adversarial robustness, some previously-ineffective adversarial examples may become misclassified, causing a regression in the perceived security of the system. We propose a novel technique, named robustness-congruent adversarial training, to address this issue. It amounts to fine-tuning a model with adversarial training, while constraining it to retain higher robustness on the adversarial examples that were correctly classified before the update. We show that our algorithm and, more generally, learning with non-regression constraints, provides a theoretically-grounded framework to train consistent estimators. Our experiments on robust models for computer vision confirm that (i) both accuracy and robustness, even if improved after model update, can be affected by negative flips, and (ii) our robustness-congruent adversarial training can mitigate the problem, outperforming competing baseline methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Extended Reality and Artificial Intelligence for Exergaming: Opportunities and Open Challenges for Rehabilitation and Cognitive Training

Extended Reality and Artificial Intelligence for Exergaming:...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Manuela Chessa Lorenzo Gerini Razeen Hussain Matteo Martini Marianna Pizzo Fabio Solari Eros Viola Dept. of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Italy

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

The increased interest in the use of Virtual, Augmented, and Mixed Reality technologies, also referred to by the wider term eXtended Reality (XR), to create effective and innovative serious games and exergames in the healthcare domain opens several questions and points out open and still unsolved issues. In this paper, we will describe the main available software and hardware technologies and discuss the opportunities and open challenges in rehabilitation. We will also discuss the need to fill the existing gap between the advancement of techniques in the XR domain and their effective use in healthcare. Specifically, we will describe some use cases recently developed and included in clinical trials.

关键词： Training Industries Technological innovation Extended reality Mixed reality Medical services Games Clinical trials Software Hardware

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AI-Based Predictive Ramp-Metering Control for Freeway Traffic systems

AI-Based Predictive Ramp-Metering Control for Freeway Traffi...

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International Conference on Intelligent Transportation

作者： K. Binjaku E. K. Meçe C. Pasquale S. Siri S. Sacone Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Computer Engineering Polytechnic University of Tirana Albania

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Traffic congestion in freeways poses significant challenges, impacting travel times and environmental sustainability. This paper proposes a novel approach to enhance ramp metering control using predictive traffic insights derived from physics-informed LSTM (Long Short-Term Memory) models. By integrating predictive capabilities with established control strategies like ALINEA, the method dynamically adjusts on-ramp flow rates based on anticipated traffic conditions. Real-world traffic data are used to evaluate the effectiveness of the approach, demonstrating improved performance compared to the adoption of conventional controllers.

关键词： Training Computational modeling Neural networks Machine learning Predictive models Traffic control Aerospace electronics Long short term memory Traffic congestion Intelligent transportation systems

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