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IEEE Latin American robotics Symposium, LARS

作者： L. C. Vinícius Faria C. G. Caio Ribeiro Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

The class of vehicle routing problems plays an important role in optimizing logistics and transportation strategies across various sectors. In this context, the Orienteering Problem (OP) seeks to determine an optimized path through a set of locations with associated rewards, aiming to maximize the total collected reward while respecting constraints such as time or distance limits. Nonholonomic motion planning, particularly considering Dubins vehicles, is crucial in real-world applications due to its ability to accurately capture the constraints and dynamics of many physical vehicles. In this paper, we extend the Dubins Orienteering Problem (DOP), where unlike the classical Dubins model with constant speed, the vehicle can vary its speed during navigation, enabling the discovery of more effective paths. Experiments comparing both the DOP and our novel formulation show that the VS-DOP achieves higher total collected rewards within the same budget compared to the conventional DOP.

关键词： Navigation Vehicle routing Dynamics Transportation Planning Time factors Vehicle dynamics Robots Logistics

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NOISE-CONDITIONED ENERGY-BASED ANNEALED REWARDS (NEAR): A GENERATIVE FRAMEWORK FOR IMITATION LEARNING FROM OBSERVATION

arXiv

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arXiv 2025年

作者： Diwan, Anish Abhijit Urain, Julen Kober, Jens Peters, Jan Department of Cognitive Robotics TU Delft Netherlands Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Germany

This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), constructs several perturbed versions of the expert’s motion data distribution and learns smooth, and well-defined representations of the data distribution’s energy function using denoising score matching. We propose to use these learnt energy functions as reward functions to learn imitation policies via reinforcement learning. We also present a strategy to gradually switch between the learnt energy functions, ensuring that the learnt rewards are always well-defined in the manifold of policy-generated samples. We evaluate our algorithm on complex humanoid tasks such as locomotion and martial arts and compare it with state-only adversarial imitation learning algorithms like Adversarial Motion Priors (AMP). Our framework sidesteps the optimisation challenges of adversarial imitation learning techniques and produces results comparable to AMP in several quantitative metrics across multiple imitation settings. Code and videos available at ***/noise-conditionedenergy-based-annealed-rewards/ © 2025, CC BY.

关键词： Adversarial machine learning

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An Augmented Reality Measurement Tool for the da Vinci Research Kit

An Augmented Reality Measurement Tool for the da Vinci Resea...

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International Symposium on Medical robotics (ISMR)

作者： Shiyue Wang Jing-Fen Wang Yubin Koh Haoying Zhou Anton Deguet Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Purdue University West Lafayette IN USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

We present an augmented reality surgical robotics console for the da Vinci Research Kit (dVRK) that integrates a real-time distance measurement tool to enhance spatial awareness during robotic-assisted surgical procedures through an interactive system. The system integrates an RViz-based interface with ROS topics, enabling virtual cursor visualization and interactive controls for precise measurements on tissue or phantom models. It supports two distinct measurement approaches: (1) a virtual measurement capability, where the operator uses the two Master Tool Manipulators (MTMs) to drive virtual 3D measurement cursors in the stereo display (a “masters as mice” mode), and (2) a physical measurement tool, where the user teleoperates one or more Patient Side Manipulators (PSMs) to touch two features and report the intervening distance. We conduct a user study on virtual (MTM) and teleoperation (PSM) modes to assess performance and accuracy and gather user feedback. The results show a Root Mean Square Error (RMSE) of 2.19 mm to 7.25 mm across different modes and tasks. This work underscores the importance of such systems for enhancing accuracy and usability in endoscopic surgery, where spatial awareness and intraoperative assessment are critical. Code is available at https://***/capricieuxV/RVinci_***.

关键词： Visualization Three-dimensional displays Medical robotics Accuracy Surgery Manipulators Real-time systems Usability Root mean square Augmented reality

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Preference detection of the humanoid robot face based on EEG and eye movement

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Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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AI-Powered Drone to Address Smart City Security Issues 37th

AI-Powered Drone to Address Smart City Security Issues

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Salama, Ramiz Al-Turjman, Fadi Culmone, Rosario Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Near East University Nicosia Mersin 10 Nicosia Turkey Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey Computer Science Division School of Science and Technology University of Camerino Camerino62032 Italy

ISBN: (纸本)9783031286933

The idea of a dazzling metropolis has drawn interest from all across the world. New innovations like blockchain, IoT, artificial intelligence, robots, and many other things were added to it. Security is one of the top issues for people living in big cities, and everyone wants to feel completely secure when traveling around every single day. In this research, we will look into how CEOs in affluent cities use and value robots, especially in terms of security. To understand the robot security the board stream, some approaches and intricacies are used. Following that is a discussion of issues with urban security and the application of artificial intelligence to drones as a management tool. The use of cutting-edge technology, such as blockchain, to support smart urban community management is covered in the last part. The smart city idea and all of its benefits for local inspection are supported by robotic use. The idea of thriving urban areas is spreading around the world and is crucial in the context of developing economies. We examined how to leverage cutting-edge technologies in this project to make it feasible. Artificial intelligence, robotics breakthroughs, and blockchain technologies all have significant effects. This research highlights their significance to analysts and how they consider them when assessing prospects. This project will be very beneficial for analysts and experts in a relevant subject. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Acoustic Neural 3D Reconstruction Under Pose Drift

arXiv

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arXiv 2025年

作者： Lin, Tianxiang Qadri, Mohamad Zhang, Kevin Pediredla, Adithya Metzler, Christopher A. Kaess, Michael the Robotics Institute Carnegie Mellon University PittsburghPA United States the Department of Computer Science the University of Maryland College Park United States The Computer Science Department Dartmouth College United States

We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation;small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we propose an algorithm that jointly optimizes the neural scene representation and sonar poses. Our algorithm does so by parameterizing the 6DoF poses as learnable parameters and backpropagating gradients through the neural renderer and implicit representation. We validated our algorithm on both real and simulated datasets. It produces high-fidelity 3D reconstructions even under significant pose drift. © 2025, CC BY.

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Entropy-Guided Reinforced Open World Active 3D Object Detection Learning

Entropy-Guided Reinforced Open World Active 3D Object Detect...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Haozhe Ma, Liyan Ying, Shihui Institute of Artificial Intelligence Shanghai University Shanghai200444 China School of Computer Engineering and Science School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China Department of Mathematics School of Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798350368604

Traditional fully annotated closed set 3D object detection methods improve model performance but are impractical in real-world settings due to the emergence of new categories and the complexity of 3D annotations. Open-World Object Detection (OWOD) addresses these issues but relies heavily on manual labeling, which is costly. This paper focuses on open world active learning and proposes an entropy-guided reinforced open world active 3D object detection (EROA). EROA regards active learning as a reinforcement learning problem tailored for open driving scenarios. We use entropy as a reward metric for efficient reinforcement learning. We also leverage knowledge from the 2D domain using object-level large-scale vision-language models to enhance sample selection. Extensive experiments evidence that the proposed EROA meets the dynamic and cost-sensitive requirements of autonomous driving, enabling real-time detection of both known and unknown objects. © 2024 IEEE.

关键词： Active learning

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Heterogeneous Wireless Access Networks for IoT and E-Health-A Survey 3

Heterogeneous Wireless Access Networks for IoT and E-Health-...

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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

In a heterogeneous wireless environment, there are many different radio access technologies (RATs), such as Wi-Fi and 2G, 3G, 4G, and 5G, which each have a different degree of coverage and processing power to meet various service requirements. A mobile user may have access to various access networks in such a situation. On the PC of each user, numerous programs with varied Quality of Service (QoS) requirements can operate simultaneously. It would be advantageous for a multi-interface terminal to use two or more interfaces simultaneously in order to improve performance. However, using multiple networks at once could use up more energy than just using one interface. Therefore, energy use must to be taken into account when analyzing the Flow/Interface Relationship (FIA). This paper presents a novel method for choosing the ideal FIA that achieves the best trade-off between all the characteristics taken into account, dubbed Smart Tabu Search (STS). STS considers user preferences, network circumstances, network expenses, application QoS specifications, and battery life of mobile devices. We use simulations and testbed experiments to validate our concept. Professionals are now able to diagnose and monitor patients remotely because to the growing use of healthcare monitoring devices. One of the most frequent occurrences that affects the reliability of information transmission in any network is congestion, which is defined as the unchecked increase in traffic relative to network capacity. With the use of a mesh network, wireless sensors, and some of the most significant models for vital signs, the article aims to provide a realistic simulation environment for a healthcare system. The simulator environment is a helpful tool for assessing the dependability and effectiveness of the healthcare system in a realistic setting. However, the system is not appropriate for real-time applications due to the sluggish network adaption caused by the end-to-end based traffic regulation deci

关键词： Electrocardiograms

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Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks 28th

Attention-Based Hybrid Deep Learning Model for Intrusion Det...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Ullah, Safi Boulila, Wadii Koubaa, Anis Ahmad, Jawad Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Computer Science Quaid-i-Azam University Islamabad44000 Pakistan RIADI Laboratory National School of Computer Science University of Manouba Manouba Tunisia School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks. © 2024 The Authors.

关键词： Network intrusion

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