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RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. A demonstration video is availab.e at https://***/playlist?list=PL7et4nGJAImLGytsJbglGbXl1hacA2dy. © 1999-2012 IEEE.

关键词： Robot vision

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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Semi-supervised feature contrast incremental learning framework for bearing fault diagnosis with limited lab.led samples

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Applied Soft Computing 2025年 176卷

作者： Tao, Xuyang Shen, Changqing Li, Lin Wang, Dong Shi, Juanjuan Zhu, Zhongkui School of Rail Transportation Soochow University Suzhou215131 China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing100190 China The State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong University Shanghai200240 China

In real-world scenarios, rotating machinery consistently introduces new fault classes, but intelligent fault diagnosis methods mostly rely on the closed-world assumption, expecting only known fault classes during testing. Moreover, obtaining a sufficient number of lab.led samples is often challenging. These challenges constrain the application and reliability of intelligent diagnosis models in real-world scenarios. Semi-supervised incremental learning enables continuous learning of new fault classes in an open environment, relying on a small number of lab.led samples and a certain number of unlab.led samples. To address the semi-supervised incremental learning problem of fault classes, semi-supervised feature contrast (SSFC) is proposed, a new approach for bearing fault diagnosis with limited lab.led samples. Specifically, a feature contrastive loss incorporating enhancement strategies is designed, independent of lab.led sample information. This approach enables the model to retain knowledge of old classes while learning about new ones. A lab.l reconstruction mechanism based on class centroids is utilized, effectively leveraging the structural information inherent in the samples to support supervised training. A dynamic class prototype cosine classifier initialized by class centroids is devised to mitigate interference between knowledge of fault classes. Finally, two incremental fault diagnosis case studies are designed to evaluate the effectiveness of the proposed method. The fault diagnosis results indicate that SSFC can continuously learn knowledge of new fault classes with limited lab.led samples and effectively alleviate catastrophic forgetting. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel state Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collab.rative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collab.rate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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Synergistic enhancement of memristive behavior and self-rectifying effect in MnO2 based memristor by ions doping method

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Applied Materials Today 2025年 44卷

作者： Yang, Bo Cao, Zelin Sun, Bai Gao, Kaikai Wang, Mengna Zhou, Guangdong Lv, Jian Luo, Sihai Chen, Xiaoliang Li, Xiangming Shao, Jinyou Frontier Institute of Science and Technology and Interdisciplinary Research Center of Frontier Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China Micro-and Nano-technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China College of Artificial Intelligence Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing400715 China

As one of the most promising candidates to replace the traditional von Neumann architecture, memristor shows considerable potential in neuromorphic computing, neural networks, brain-like chips, artificial intelligence, and information storage. However, its performance in terms of ultra-high stability, scalab.lity, and extended switching windows has yet to be broken through, limiting its practical applications. In this work, it is investigated in detail the effect of Cu2+/Zn2+ ions doping on the memristive performance in the Ag/δ-MnO2/Ti devices with three different doping concentrations. We found that the optimal doping concentration can significantly improve the stability and resistance switching ratio of the device. Through first principles analysis, it is further confirmed that the main mechanism for the improvement in device switching ratio is the reduction of MnO bandgap caused by Cu2+/Zn2+ ions doping. Finally, we proposed that the formation of CuO/ZnO buffer layer through the interaction between Cu2+/Zn2+ ions and O2- ions is the key factor leading to the transition of threshold voltage from self-rectification to memristive behavior. Therefore, this work provides an in-deep understanding of the theory of metal ions doping in memristive device, which adds a new strategy to improve the memristive performance of the device in neuromorphic applications. © 2025 Elsevier Ltd

关键词： Electric rectifiers

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Identification for Switched systems

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IFAC-PapersOnLine 2015年第28期48卷 514-519页

作者： Hu, Qiong Fei, Qing Ma, Hongbin Wu, Qinghe Geng, Qingbo State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

For a class of unstable discrete-time multi-variable switched systems with parametric uncertainty, an identification scheme is developed in this paper. Specifically, the identification algorithm is derived to identify the parameter vectors of potential subsystems based on the idea of least geometric mean squares. Furthermore, an neural network classifier is put forward for partition of the identified subsystems. Finally, feasibility and effectiveness of the proposed identification scheme are verified by numerical simulations, which show that the desirable identification performance is guaranteed with the proposed scheme. © 2015

关键词： Parameter estimation Computational fluid dynamics Geometric mean Identification algorithms Identification scheme Multi variables Neural network classifier Parameter vectors Parametric uncertainties Switched system

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Accelerating Cross-Scenario Metasurface Adaptability with Plug-and-Play Kernel

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Advanced Functional Materials 2025年

作者： Wu, Nanxuan Qian, Chao Wang, Zhedong Zhu, Xiaoyue Xiao, Cheng Li, Erping Chen, Hongsheng ZJU-UIUC Institute Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Hangzhou310027 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Zhejiang Province Key Laboratory of Intelligent Vehicle Electronics Hangzhou Dianzi University Hangzhou310018 China

The adaptivity of metasurfaces depicts an important behavior of dynamically adjusting their internal properties to adapt to changing environments and cater to various demands. In spite of the proliferation of intelligence-driven metasurfaces, they still lack the analogical reasoning to execute cross-scenario tasks due to the random and unpredictable scattering events. Here, a compatible plug-and-play scheme is proposed to assist metasurfaces adapt from one to another environment. Unlike conventional step-by-step training, such a scheme enables the rapid establishment of reliable neural networks with physical interpretability by integrating past experience into a package kernel. Taking the invisibility cloak as a demonstration, a fully intelligent system is set up, and carried out a three-stage adaptability validation: point-to-point, path-to-path, and scene-to-scene, suggesting a substantial speed improvement compared with direct learning. Furthermore, by capitalizing on the resonant metasurfaces, the plug-and-play kernel is successfully extended to broadband performance with a relative bandwidth of 50%. work provides a powerful and easy-to-access recipe for advancing metasurfaces in new and even unknown environments and fosters the shining and bloom of intelligent adaptive meta-devices. © 2025 Wiley-VCH GmbH.

关键词： Computer music

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An analysis of Beijing HFMD patients mobility pattern during seeking treatment

An analysis of Beijing HFMD patients mobility pattern during...

引用

2013 International Conference for Smart Health, ICSH 2013

作者： Zhu, Zhaonan Cao, Zhidong Zeng, Daniel Dajun State Key Lab. of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642398438

Previous epidemiological researches have studied HFMD transmission pattern, but the study on the patients mobility pattern while seeking treatment is absent. In this paper, we present a statistical analysis of the spatialtemporal pattern of the Beijing HFMD patients mobility based on a complete works dataset containing over 40,000 cases in 2010. The main findings are as follows: (1) the patents incline to take their sick children to hospitals on weekdays, especially on Monday;(2) the patients living far from city center are more likely to get to hospitals in the afternoon, while the patients in the downtown in the morning;(3) patients (or their parents) either select the nearby hospitals, or the Class-A hospitals in the downtown. Furthermore, we employ a gravity model to describe the spatial mobility pattern of patients. The experiment results show a good fit. © 2013 Springer-Verlag.

关键词： Hospitals

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