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arXiv 2025年

作者： Wang, Luming Shi, Hao Yin, Xiaoting Yang, Kailun Wang, Kaiwei Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Egocentric gesture recognition is a pivotal technology for enhancing natural human-computer interaction, yet traditional RGB-based solutions suffer from motion blur and illumination variations in dynamic scenarios. While event cameras show distinct advantages in handling high dynamic range with ultra-low power consumption, existing RGB-based architectures face inherent limitations in processing asynchronous event streams due to their synchronous frame-based nature. Moreover, from an egocentric perspective, event cameras record data that includes events generated by both head movements and hand gestures, thereby increasing the complexity of gesture recognition. To address this, we propose a novel network architecture specifically designed for event data processing, incorporating (1) a lightweight CNN with asymmetric depthwise convolutions to reduce parameters while preserving spatiotemporal features, (2) a plug-and-play state-space model as context block that decouples head movement noise from gesture dynamics, and (3) a parameter-free Bins-Temporal Shift Module (BSTM) that shifts features along bins and temporal dimensions to fuse sparse events efficiently. We further establish the EgoEvGesture dataset, the first large-scale dataset for egocentric gesture recognition using event cameras. Experimental results demonstrate that our method achieves 62.7% accuracy tested on unseen subjects with only 7M parameters, 3.1% higher than state-of-the-art approaches. Notable misclassifications in freestyle motions stem from high interpersonal variability and unseen test patterns differing from training data. Moreover, our approach achieved a remarkable accuracy of 97.0% on the DVS128 Gesture, demonstrating the effectiveness and generalization capability of our method on public datasets. The dataset and models are made available at https://***/3190105222/EgoEv_Gesture. Copyright © 2025, The Authors. All rights reserved.

关键词： Gesture recognition

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Based on an improved Zero-DCE++ low-light enhanced object detection algorithm

Based on an improved Zero-DCE++ low-light enhanced object de...

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Bai, Xuecheng Gao, Hongwei Zhao, Zhanpeng Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China Xi'an Modern Control Technology Research Institute Xi'an710100 China

ISBN: (纸本)9781510689176

Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information loss. This study presents an enhanced low-light image detection model based on an improved Zero-DCE++ algorithm, integrated with YOLOv8 for high-speed, accurate detection. key improvements include the CBAM attention mechanism to enhance feature extraction in low-light areas, DCNv2 for capturing complex object details, and a Local Region Brightness Curve Enhancement Module to prevent overexposure in well-lit areas. The proposed model was tested on the Night Drone dataset, where it achieved superior performance in image quality and detection accuracy compared to state-of-the-art methods. This approach demonstrates significant potential for applications requiring reliable object detection in challenging low-light conditions. © 2025 SPIE.

关键词： Semantic Segmentation

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Design, Dynamic Modeling and control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

arXiv

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arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts

arXiv

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arXiv 2025年

作者： Huang, Yizhou Yang, Fan Zhu, Guoliang Li, Gen Shi, Hao Zuo, Yukun Chen, Wenrui Li, Zhiyong Yang, Kailun School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China School of Informatics University of Edinburgh United Kingdom State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China

Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions. This information is rich and multimodal in nature. Existing multimodal affordance methods face limitations in extracting useful information, mainly due to simple structural designs, basic fusion methods, and large model parameters, making it difficult to meet the performance requirements for practical deployment. To address these issues, this paper proposes the BiT-Align image-depth-text affordance mapping framework. The framework includes a Bypass Prompt Module (BPM) and a Text Feature Guidance (TFG) attention selection mechanism. BPM integrates the auxiliary modality depth image directly as a prompt to the primary modality RGB image, embedding it into the primary modality encoder without introducing additional encoders. This reduces the model’s parameter count and effectively improves functional region localization accuracy. The TFG mechanism guides the selection and enhancement of attention heads in the image encoder using textual features, improving the understanding of affordance characteristics. Experimental results demonstrate that the proposed method achieves significant performance improvements on public AGD20K and HICO-IIF datasets. On the AGD20K dataset, compared with the current state-of-the-art method, we achieve a 6.0% improvement in the KLD metric, while reducing model parameters by 88.8%, demonstrating practical application values. The source code will be made publicly available at https://***/DAWDSE/BiT-Align. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Machine Learning for Estimation and control of Quantum systems

arXiv

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arXiv 2025年

作者： Ma, Hailan Qi, Bo Petersen, Ian R. Wu, Re-Bing Rabitz, Herschel Dong, Daoyi the School of Engineering Australian National University CanberraACT2601 Australia the Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China the Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China the Department of Chemistry Princeton University PrincetonNJ08544 United States

The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control, calibration, and validation of quantum states and their dynamics. Machine learning methods have emerged as powerful tools owing to their remarkable capability to learn from data, and thus have been extensively utilized for different quantum tasks. This paper reviews several significant topics related to machine learning-aided quantum estimation and control. In particular, we discuss neural networks-based learning for quantum state estimation, gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, machine learning for quantum robust control, and reinforcement learning for quantum control. This review provides a brief background of key concepts recurring across many of these approaches with special emphasis on neural networks, evolutionary computation, and reinforcement learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Torque Coordinated control of Six-Wheeled Planetary Rovers Based on Wheel-Terrain Interaction

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Yang, Huaiguang Yang, Lintao Ding, Liang Yang, Chaojie Xu, Chongfu Gao, Haibo Yuan, Ye Deng, Zongquan Harbin Institute of Technology Harbin150001 China Huawei Technologies Co.Ltd. Shanghai200000 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China Student Affairs Office Harbin Institute of Technology Harbin150001 China

Global interest in lunar exploration programs has heightened focus on the lunar South Pole region. With its complex terrain and irregular illumination, this region poses challenges for the mobility and energy management of planetary rovers. Consequently, to ensure adequate passing ability while minimizing energy consumption, research on the coordinated control of multi-driven wheels is essential. In this study, a torque-coordinated control approach based on wheel-terrain interaction for a six-wheeled planetary rover is proposed. First, the continuous stress distribution model of the wheel-terrain interaction is simplified into an equivalent concentrated force model. Subsequently, precise and robust tracking of the desired velocity was achieved using a sliding-mode controller to overcome the challenges posed by model simplification and rough terrain. Subsequently, based on the expected torque obtained from the sliding-mode controller, with the optimization objective of minimizing energy consumption, the forces on the wheels were analyzed using the equivalent concentrated force model, and the torque distribution was coherently controlled. Finally, the effectiveness of the control method and the accuracy of the model are verified through whole-vehicle experiments, and the energy efficiency optimization ratio varies from 9.93% to 5.16% within the resistance range of 0-80N. © 2024 IEEE.

关键词： Rovers

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Scalable Fabrication of Uniform Fast-Response Humidity Field Sensing Array for Respiration Recognition and Contactless Human-Machine Interaction

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Advanced Functional Materials 2025年

作者： Zhang, Zhicheng Li, Jianye Chen, Huizhi Wang, Hao Luo, Yibing Si, Ranzi Xie, Ruijie Tao, Kai Yang, Bo-Ru Zhang, Daohui Liu, Fei Huo, Fengwei Wu, Jin State Key Laboratory of Optoelectronic Materials and Technologies and the Guangdong Province Key Laboratory of Display Material and Technology School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Xiamen University Xiang'an Campus Xiang'an South Road Fujian Xiamen361102 China Dongguan Key Laboratory of Advanced Drug Delivery and Biosensing Research and Development School of Pharmacy and Dongguan Innovation Institute Guangdong Medical University Dongguan523808 China Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical Engineering Northwestern Polytechnical University Xi'an710072 China State Key Laboratory of Transducer Technology Shanghai200050 China

In the wake of the COVID-19 pandemic, there is an increased demand for humidity sensors that can accurately detect targets without direct contact, driving advancements in contactless human-machine interaction (HMI) and non-invasive medical diagnostics. However, it is difficult for traditional individual sensors to accurately acquire humidity field information for reliable HMI. Here, an ionogel film-based flexible and fast-response humidity field sensing array is developed via a scalable, efficient, and modified spin-coating-based fabrication strategy for accurate detection of humidity distribution. By optimizing the structure and constitution of the hydrophobic ionogel films, the sensors display ultrafast response and recovery time (0.65/0.85 s), a broad detection range (11-98% RH), long-term stability (120 days), excellent repeatability, flexibility, and environmental tolerances. Thanks to the one-time scalable fabrication, the sensing units in the array exhibit superior uniformity, as the device-to-device deviation is reduced to one-ninth compared to traditional multi-batch fabrication methods. With the aid of machine learning algorithms, this humidity sensing array realizes not only the accurate identification of various subtle breath abnormalities (e.g., oral breathing, apnea, left and right nasal congestions) by conformally attaching to wearable masks, but also the precise contactless HMI applications (e.g., anti-interference gesture recognition and wireless control of intelligent executive terminals). © 2025 Wiley-VCH GmbH.

关键词： Humidity sensors

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： networked control systems

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