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1st IEEE Conference on Telepresence, Telepresence 2024

作者： Singh, Harsimran Marcilla, Luis Perez Banerjee, Premankur Rothammer, Michael Hulin, Thomas The Institute of Robotics and Mechatronics Munich Germany Dresden51147 Germany University of Southern California Department of Computer Science United States Hapticlabs Germany

ISBN: (纸本)9798331528249

This paper presents the proof of concept of the modular nature of ferrofluid based tactile device, FerroVibe. The modularity of FerroVibe allows for customization in degrees of freedom, force feedback, and vibrational cues by strategically positioning magnetic actuators around the central assembly containing a neodymium magnet and ferrofluid. Three distinct configurations of FerroVibe are introduced, each offering unique actuation mechanisms and physical characteristics that could be tailored to different application requirements. The paper showcases the proof of concept and details the working principle of each configuration, highlighting the strengths and limitations of each design. Initial comparative analysis is conducted to evaluate the trade-offs in terms of power consumption, speed, size, weight, and feedback fidelity. The results demonstrate that FerroVibe's potential modular architecture provides a flexible and effective solution for a wide range of tactile feedback applications, making it a promising candidate for future advancements in haptic technology. © 2024 IEEE.

关键词： Magnetic actuators

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India's Stake in Emerging Technology - A Review 18

India's Stake in Emerging Technology - A Review

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Singh, Sugandha China Ramu, S. Lalit, Ruchika Sgt University Department of Computer Science and Engineering Gurugram India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India University School of Automation and Robotics Ggsipu East Campus New Delhi India

ISBN: (纸本)9789380544519

Emerging technologies of today are altering the way we think about the built environment. In the meantime, many technologies have undergone revolutionary changes, and India, an expanding nation, offers opportunities for development in several new areas. The goal of the study is to show how organizations need to adapt their perspective and create new strategies for emerging technologies such as artificial intelligence (AI), machine learning (ML), big data, quantum, and space exploration in light of the swift advancements in technology. This paper presents that although India has come across a lot of advancements in this era of emerging technology, certain challenges have been faced in aspects like adapting, feasibility, and approval which brought many changes across the country, which in turn is impacting the pace to transform the country towards Industry 4.0 and Industry 5.0. Therefore a study is presented to understand the impact of emerging technologies for countries coming up as Industry 4.0 and Industry 5.0. This paper emphasizes on for India's initiatives for emerging technologies to come up as VIKSIT BHARAT @2047. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Intelligence Big Data Fabrication Hypersonic platforms Industry 4.0 and Industry 5.0 Quantum computing

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33rd ACM Web Conference, WWW 2024

作者： Ma, Changyi Yu, Runsheng Zhang, Youzhi Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation CAS Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400701719

The similarity matrix is at the core of similarity search problems. However, incomplete observations are ubiquitous in real scenarios leading to a less accurate similarity matrix. To alleviate this problem, in this paper, based on the key insight that the similarity matrix enjoys both the symmetric and positive semi-definiteness (PSD) properties, we propose a novel similarity matrix calibration method, which is scalable, effective, and sound. Specifically, we establish the PSD property as a constraint for the similarity matrix calibration problem and propose a novel similarity matrix calibration method to estimate the similarity matrix, which approximates the unknown complete ground-truth similarity matrix. To enable a fast optimization process, we further develop a general approximated algorithm that bypasses the computation of singular values. Theoretical analysis ensures stable calibration performance and convergence speed. Extensive experiments of similarity matrix calibration on real-world datasets demonstrate that our proposed method outperforms baseline methods in terms of both accuracy and speed. © 2024 ACM.

关键词： Calibration

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Improving Sharpness-Aware Minimization by Lookahead 41

Improving Sharpness-Aware Minimization by Lookahead

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Runsheng Zhang, Youzhi Kwok, James T. Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong

Sharpness-Aware Minimization (SAM), which performs gradient descent on adversarially perturbed weights, can improve generalization by identifying flatter minima. However, recent studies have shown that SAM may suffer from convergence instability and oscillate around saddle points, resulting in slow convergence and inferior performance. To address this problem, we propose the use of a lookahead mechanism to gather more information about the landscape by looking further ahead, and thus find a better trajectory to converge. By examining the nature of SAM, we simplify the extrapolation procedure, resulting in a more efficient algorithm. Theoretical results show that the proposed method converges to a stationary point and is less prone to saddle points. Experiments on standard benchmark datasets also verify that the proposed method outperforms the SOTAs, and converge more effectively to flat minima. Copyright 2024 by the author(s)

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Graph-Based Feature Learning for Cross-Project Software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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How Language of Interaction Affects the User Perception of a Robot 15th

How Language of Interaction Affects the User Perception of...

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15th International Conference on Social robotics, ICSR 2023

作者： Sienkiewicz, Barbara Sejnova, Gabriela Gajewski, Paul Vavrecka, Michal Indurkhya, Bipin Cognitive Science Department Jagiellonian University Krakow Poland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic Institute of Computer Science AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9789819987146

Spoken language is the most natural way for a human to communicate with a robot. It may seem intuitive that a robot should communicate with users in their native language. However, it is not clear if a user’s perception of a robot is affected by the language of interaction. We investigated this question by conducting a study with twenty-three native Czech participants who were also fluent in English. The participants were tasked with instructing the Pepper robot on where to place objects on a shelf. The robot was controlled remotely using the Wizard-of-Oz technique. We collected data through questionnaires, video recordings, and a post-experiment feedback session. The results of our experiment show that people perceive an English-speaking robot as more intelligent than a Czech-speaking robot (z = 18.00, p-value = 0.02). This finding highlights the influence of language on human-robot interaction. Furthermore, we discuss the feedback obtained from the participants via the post-experiment sessions and its implications for HRI design. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feedback

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Towards a More Stable and General Subgraph Information Bottleneck 48

Towards a More Stable and General Subgraph Information Bottl...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Hongzhi Zheng, Kaizhong Yu, Shujian Chen, Badong Xi'an Jiaotong University Institute of Artifical Intelligience and Robotics China Vrije Universiteit Amsterdam Department of Computer Science Netherlands

ISBN: (纸本)9781728163277

Graph Neural Networks (GNNs) have been widely applied to graph-structured data. However, the lack of interpretability impedes its practical deployment especially in high-risk areas such as medical diagnosis. Recently, the Information Bottleneck (IB) principle has been extended to GNNs to identify a compact subgraph that is most informative to class labels, which significantly improves the interpretability on decision. However, existing Graph Information Bottleneck (GIB) models are either unstable during the training (due to the difficulty of mutual information estimation) or only focus on a special kind of graph (e.g., brain networks) that suffer from poor generalization to general graph datasets with varying graph sizes. In this work, we extend the recently developed Brain Information Bottleneck (BrainIB) to general graphs by introducing matrix-based Rényi's α-order mutual information to stablize the training;and by designing a novel mask strategy to deal with varying graph sizes such that the new method can also be used for social networks, molecules, etc. Extensive experiments on different types of graph datasets demonstrate the superior stability and generality of our model. © 2023 IEEE.

关键词： Generalization Graph Information Bottleneck Stability

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Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning 8

Bridging the gap between Learning-to-plan, Motion Primitives...

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8th Conference on Robot Learning, CoRL 2024

作者： Kicki, Piotr Tateo, Davide Liu, Puze Guenster, Jonas Peters, Jan Walas, Krzysztof IDEAS NCBR Warsaw Poland Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Department of Computer Science Technische Universitat Darmstadt Germany

Trajectory planning under kinodynamic constraints is fundamental for advanced robotics applications that require dexterous, reactive, and rapid skills in complex environments. These constraints, which may represent task, safety, or actuator limitations, are essential for ensuring the proper functioning of robotic platforms and preventing unexpected behaviors. Recent advances in kinodynamic planning demonstrate that learning-to-plan techniques can generate complex and reactive motions under intricate constraints. However, these techniques necessitate the analytical modeling of both the robot and the entire task, a limiting assumption when systems are extremely complex or when constructing accurate task models is prohibitive. This paper addresses this limitation by combining learning-to-plan methods with reinforcement learning, resulting in a novel integration of black-box learning of motion primitives and optimization. We evaluate our approach against state-of-the-art safe reinforcement learning methods, showing that our technique, particularly when exploiting task structure, outperforms baseline methods in challenging scenarios such as planning to hit in robot air hockey. This work demonstrates the potential of our integrated approach to enhance the performance and safety of robots operating under complex kinodynamic constraints. © 2024 Proceedings of Machine Learning Research.

关键词： Reinforcement learning

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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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Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson’s Disease Using Gait Signals

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SN computer science 2023年第2期4卷 1-12页

作者： Klinton Amaladass, P. Subathra, M.S.P. Jeba Priya, S. Sivakumar, Mahima Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

One of the most common ailments, especially among the elderly, is Parkinson's disease (PD). Although previous research has demonstrated that heuristics can diagnose Parkinson's disease using decisive signs like tremor, muscular rigidity, movement disorders, and voice disorders, it has also been reported that current approaches, which rely on simple motor tasks, are limited and lack stability and accessibility. The purpose of this study is to identify a novel cost-effective and time-efficient early detection technique for the prediction of this disease using a signal processing feature extraction approach namely, Shifted Extended Local Binary Pattern (S-ELBP) using gait signals. The features extracted using the proposed methods are given as the input to an artificial neural network (ANN) to classify them as Healthy or Parkinson’s. The proposed method has quite promising results when evaluated using different performance metrics. The method has yielded accuracy: 97.6%, specificity: 95.71%, sensitivity: 99, positive predictive value (PPV): 97.2%, negative predictive value (NPV): 98.8%, Matthews correlation coefficient (MCC): 95.4%, F1-score: 97.9%, and geometric mean: 97.19%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Parkinson’s disease Parkinson’s gait Shifted extended local binary pattern

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