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arXiv 2025年

作者： Chen, Jiaming Zhao, Wentao Meng, Ziyu Mao, Donghui Song, Ran Pan, Wei Zhang, Wei School of Control Science and Engineering Shandong University China Department of Computer Science The University of Manchester United Kingdom

Model Predictive control (MPC) is a widely adopted control paradigm that leverages predictive models to estimate future system states and optimize control inputs accordingly. However, while MPC excels in planning and control, it lacks the capability for environmental perception, leading to failures in complex and unstructured scenarios. To address this limitation, we introduce Vision-Language Model Predictive control (VLMPC), a robotic manipulation planning framework that integrates the perception power of vision-language models (VLMs) with MPC. VLMPC utilizes a conditional action sampling module that takes a goal image or language instruction as input and leverages VLM to generate candidate action sequences. These candidates are fed into a video prediction model that simulates future frames based on the actions. In addition, we propose an enhanced variant, Traj-VLMPC, which replaces video prediction with motion trajectory generation to reduce computational complexity while maintaining accuracy. Traj-VLMPC estimates motion dynamics conditioned on the candidate actions, offering a more efficient alternative for long-horizon tasks and real-time applications. Both VLMPC and Traj-VLMPC select the optimal action sequence using a VLM-based hierarchical cost function that captures both pixel-level and knowledge-level consistency between the current observation and the task input. We demonstrate that both approaches outperform existing state-of-the-art methods on public benchmarks and achieve excellent performance in various real-world robotic manipulation tasks. Code is available at https://***/PPjmchen/VLMPC. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Bayes and Biased Estimators Without Hyper-Parameter Estimation: Comparable Performance to the Empirical-Bayes-Based Regularized Estimator

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IEEE control Systems Letters 2025年 9卷 294-299页

作者： Yue Ju Bo Wahlberg Håkan Hjalmarsson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems and the Competence Centre for Advanced BioProduction by Continuous Processing AdBIOPRO School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Regularized system identification has become a significant complement to more classical system identification. It has been numerically shown that kernel-based regularized estimators often perform better than the maximum likelihood estimator in terms of minimizing mean squared error (MSE). However, regularized estimators often require hyper-parameter estimation. This letter focuses on ridge regression and the regularized estimator by employing the empirical Bayes hyper-parameter estimator. We utilize the excess MSE to quantify the MSE difference between the empirical-Bayes-based regularized estimator and the maximum likelihood estimator for large sample sizes. We then exploit the excess MSE expressions to develop both a family of generalized Bayes estimators and a family of closed-form biased estimators. They have the same excess MSE as the empirical-Bayes-based regularized estimator but eliminate the need for hyper-parameter estimation. Moreover, we conduct numerical simulations to show that the performance of these new estimators is comparable to the empirical-Bayes-based regularized estimator, while computationally, they are more efficient.

关键词： Bayes methods Maximum likelihood estimation Covariance matrices Vectors System identification Noise measurement Linear regression Probability density function Contracts Computational efficiency

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Optimization of BLE Broadcast Mode in Offline Finding Network

arXiv

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arXiv 2025年

作者： Zhang, Li Feng, Cheng Xia, Tian School of Computer and Control Engineering Northeast Forestry University China School of Measurement-control Technology and Communications Engineering Harbin University of Science and Technology China

In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud servers, thereby achieving offline finding of lost items. This process is essentially a Bluetooth low energy (BLE) neighbor discovery process(NDP). In the process, the variety of Bluetooth scan modes caused by the scan interval and scan window settings affects the discovery latency of finder devices finding the tag broadcast packets. To optimize the experience of searching for lost devices, we propose the CPBIS-mechanism, a certain proportion broadcast-intervals screening mechanism that calculates the most suitable two broadcast intervals and their proportion for offline tags. This reduces discovery latency in the BLE NDP, improves the discovery success rate, further enhances the user experience. To our knowledge, we are the first to propose a comprehensive solution for configuring the broadcast interval parameters of advertisers in BLE NDP, particularly for configurations involving two or more broadcast intervals. We evaluated the results obtained by CPBIS on the nRF52832 chip. The data shows that the CPBIS-mechanism achieves relatively low discovery latencies for multiple scan modes. © 2025, CC BY.

关键词： Bluetooth

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Delayed Transitions in GPenSIM 1st

Delayed Transitions in GPenSIM

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1st International Symposium on Systems Modelling and Simulation, SMS 2024

作者： Feng, Yuming Samolej, Slawomir Davidrajuh, Reggie School of Computer Science and Engineering Chongqing Three Gorges University Wenzhou China Department Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland Department Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (纸本)9789819646128

The General-purpose Petri Net Simulator (GPenSIM) is a tool for modeling, simulation, and performance analysis of discrete event systems. GPenSIM is specially designed to model real-life industrial systems. Hence, the transitions in GPenSIM represent real-life events, such as machines. However, this induces a problem, namely that an enabled transition consumes tokens from its input places immediately. This act of an enabled transition immediately consuming tokens gives only monetarily visibility to the tokens, which can be a problem for certain cases where we need to see the tokens in their input places for a while. This paper proposes a method to delay an enabled transition ("delayed transitions") so that the tokens it is supposed to consume from its input places will be visible for a while. This paper, after presenting the theory and the method, also presents an application (the cat and mouse problem) of the delayed transitions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete event simulation

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Application Interface for GPenSIM 1st

Application Interface for GPenSIM

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1st International Symposium on Systems Modelling and Simulation, SMS 2024

作者： Feng, Yuming Samolej, Slawomir Davidrajuh, Reggie School of Computer Science and Engineering Chongqing Three Gorges University Wenzhou China Department of Computer and Control Engineering Rzeszow University of Technology Rzeszow Poland Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

ISBN: (纸本)9789819646128

The General-purpose Petri Net Simulator (GPenSIM) is a tool used by researchers to model, simulate, and analyze the performance of discrete event systems. Its popularity stems from its simple interface, extensibility, and compatibility with the MATLAB platform. This paper focuses on the crucial role of GPenSIM in Petri Net model-based software-to-software and software-to-hardware access and control, highlighting unique features and capabilities in its application interface. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： MATLAB

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Decoupling While Coupling: Towards More Accurate Stereo Image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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Semantic-aware dynamic scene tracking and reconstruction for mobile robots leveraging visual SLAM

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Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Liu, Tianwei Luo, Jingwen School of Information Science and Technology Yunnan Normal University 768 Juxian Street Yunnan Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province 768 Juxian Street Yunnan Kunming650500 China

Mobile robots operating in dynamic environments often encounter challenges due to interference from moving objects, compromising the performance of their SLAM systems. To address this issue, we propose a depth camera based semantic-aware visual SLAM approach for mobile robots, aimed at dynamic scene tracking and reconstruction. By leveraging a lightweight Yolact++ network, we segment and detect objects in the scene, eliminate dynamic features through prior knowledge and epipolar constraints, and utilize high-confidence static features for robot’s pose estimation. Subsequently, the overall similarity between images is evaluated based on the similarity of the matched semantic object regions between images to select high-quality candidate keyframes, effectively enhancing the precision of loop closure detection. Furthermore, we employ Delta Generalized Labeled Multi-Bernoulli (Delta-GLMB) filtering to track the masks of prior dynamic objects provided by Yolact++. A global semantic octree map is then constructed, integrating both the semantic knowledge of the static scene and the tracking information of dynamic targets, which in turn realizes the tracking & reconstruction of the dynamic scene. This allows mobile robots to mimic human behavior and cognitive abilities, not merely to construct a map of unknown environments more efficiently, but also to track the state of moving targets in the field-of-view(FoV) in real-time, enabling self-localization and navigation functions. Experimental results demonstrate the effectiveness of our approach in improving the robustness, accuracy, and adaptability of mobile robots in dynamic environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Mobile robots

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Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization

arXiv

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arXiv 2025年

作者： Pezone, Francesco Barbarossa, Sergio Caire, Giuseppe Di Lorenzo, Paolo Kountouris, Marios Bennis, Mehdi Sapienza University of Rome Department of Computer Control and Management Engineering Italy Technische Universität Berlin Faculty 4 - Electrical Engineering and Computer Science Germany

As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart sensors, IoT systems etc., are gaining more and more interest and new communication paradigms are needed. This thesis addresses these challenges by combining semantic communication with generative models to optimize image compression and resource allocation in edge networks. Unlike traditional bit-centric communication systems, semantic communication prioritizes the transmission of meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit of the focus solely on the relevant parts of the data due to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression algorithms, utilizing advanced generative models such as Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These algorithms compress images by encoding only semantically relevant features and exploiting the generative power at the receiver side. This allows for the accurate reconstruction of high-quality images with minimal data transmission. The thesis also introduces a Goal-Oriented edge network optimization framework based on the Information Bottleneck problem and stochastic optimization, ensuring that communication resources are dynamically allocated to maximize efficiency and task performance. By integrating semantic communication into edge networks, the proposed system achieves a balance between computational efficiency and communication effectiveness, making it particularly suited for real-time applications. The thesis compares the performance of these semantic communication models with conventional image compression techniques, using both classical and semantic-awar

关键词： Image compression

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