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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bin He Weichen Dai Zeyu Wan Hong Zhang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science Hangzhou Dianzi University Hangzhou China Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measurements. The lateral acceleration constraint is further added to trajectory estimation to make the estimated trajectory follow the motion characteristics of vehicles. In addition, since vehicle model parameters vary with different motion conditions and tyre pressure, we estimate vehicle correction factors that rectify changes in vehicle model parameters online, and also analyze the observability of these vehicle correction factors. In experiments, the proposed method is evaluated and compared with state-of-the-art methods in the public dataset. The experimental results show that the proposed method achieves more accurate results in all sequences since we add additional sensor measurements and utilize the characteristic of vehicle motion to restrict the trajectory estimation. The ablation study also proved the effectiveness of continuous-time representation, online correction factor estimation, and incorporation of lateral acceleration constraint.

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Roblets: Robotic Tablets That Self-Assemble and Self-Fold into a Robot

Roblets: Robotic Tablets That Self-Assemble and Self-Fold in...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Junyi Han Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in silico Medicine

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

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Resource Allocation for Cognitive Radio Inspired Non-Orthogonal Multiple Access Networks: A Quantum Soft Actor-Critic Method

Resource Allocation for Cognitive Radio Inspired Non-Orthogo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Huang Ying He Fei Yu Peigen Zeng Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the growth of the communication industry, the demand for spectrum resources has been increasing steadily. However, the spectrum resources are limited and the utilization rate is low. In this case, Cognitive Radio (CR) technologies and Non-Orthogonal Multiple Access (NOMA) technology are proposed to improve the number of user access and spectrum resource utilization. In this paper, we consider a CR-inspired NOMA network and propose a power allocation problem based on a time-varying system. Due to the dynamic nature of CR, traditional methods are often faced with problems such as low training efficiency and high network volatility. To address the problem, we propose a novel Quantum Reinforcement Learning (QRL) algorithm with quantum world model, interacting with the environment in the quantum way. Specifically, we construct a quantum soft actor-critic algorithm using variable quantum circuit (VQC), and the experience data is encoded into a quantum sum tree and transformed into quantum world model. Simulation results show the superior performance of the proposed framework.

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Improving Multi-Person Pose Tracking with A Confidence Network

arXiv

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arXiv 2023年

作者： Fu, Zehua Zuo, Wenhang Hu, Zhenghui Liu, Qingjie Wang, Yunhong The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Hangzhou Innovation Institute Behang University Hangzhou310051 China

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bbox-revision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Nonreciprocal field transformation with active acoustic metasurfaces

arXiv

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arXiv 2023年

作者： Wen, Xinhua Cho, Choonlae Zhu, Xinghong Park, Namkyoo Li, Jensen Department of Physics The Hong Kong University of Science and Technology Kowloon Hong Kong Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

Field transformation, as an extension of the transformation optics, provides a unique means for nonreciprocal wave manipulation, while the experimental realization remains a significant challenge as it requires stringent material parameters of the metamaterials, e.g., purely nonreciprocal bianisotropic parameters. Here, we develop and demonstrate a nonreciprocal field transformation in a 2D acoustic system, using an active metasurface that can independently control all constitutive parameters and achieve purely nonreciprocal Willis coupling. The field-transforming metasurface enables tailor-made field distribution manipulation, achieving localized field amplification by a predetermined ratio. Interestingly, the metasurface demonstrates the self-adaptive capability to various excitation conditions and can extend to other geometric shapes. The metasurface also achieves nonreciprocal wave propagation for internal and external excitations, demonstrating a one-way acoustic device. Such a field-transforming metasurface not only extends the framework of the transformation theory for nonreciprocal wave manipulation, but also holds significant potential in applications such as ultra-sensitive sensors and nonreciprocal communication. Copyright © 2023, The Authors. All rights reserved.

关键词： Acoustic fields

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M3OFF: Module-Compositional Model-Free Computation Offloading in Multi-Environment MEC

M3OFF: Module-Compositional Model-Free Computation Offloadin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Tao Ren Zheyuan Hu Jianwei Niu Weikun Feng Hang He State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Modelfree computation OFFloading (M 3 OFF) framework. M 3 OFF generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for M 3 OFF, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that M 3 OFF outperforms the performances of most state-of-the-arts in multi-MEC and reaches close to single-MEC.

关键词： Training Visualization Multi-access edge computing Computational modeling Interference Bandwidth Stability analysis

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FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding

FedTC: Enabling Communication-Efficient Federated Learning v...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated learning (FL) enables distributed training via periodically synchronizing model updates among participants. Communication overhead becomes a dominant constraint of FL since participating clients usually suffer from limited bandwidth. To tackle this issue, top-k based gradient compression techniques are broadly explored in FL context, manifesting powerful capabilities in reducing gradient volumes via picking significant entries. However, previous studies are primarily conducted on the raw gradients where massive spatial redundancies exist and positions of non-zero (top-k) entries vary greatly between gradients, which both impede the achievement of deeper compressions. Top-k may also degrade the performance of trained models due to biased gradient estimations. Targeting the above issues, we propose FedTC, a novel transform coding based compression framework. FedTC transforms gradients into a new domain with more compact energy distributions, which facilitates reducing spatial redundancies and biases in subsequent sparsification. Furthermore, non-zero entries across clients from different rounds become highly aligned in the transform domain, motivating us to partition the gradients into smaller entry blocks with various alignment levels to better exploit these alignments. Lastly, positions and values of non-zero entries are independently compressed in a block-wise manner with our customized designs, through which a higher compression ratio is achieved. Theoretical analysis and extensive experiments consistently demonstrate the effectiveness of our approach.

关键词： Training Quantization (signal) Costs Federated learning Redundancy Transform coding Estimation

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Quantum Reinforcement Learning with Quantum World Model

Quantum Reinforcement Learning with Quantum World Model

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Victor C.M. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.

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HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models

HIR-Diff: Unsupervised Hyperspectral Image Restoration Via I...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Li Pang Xiangyu Rui Long Cui Hongzhong Wang Deyu Meng Xiangyong Cao School of Computer Science and Technology Xi'an Jiaotong University Xi'an China MOEKLINNS Laboratory Xi'an Jiaotong University Xi'an China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa Macao

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Hyperspectral image (HSI) restoration aims at recov-ering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limitations in accurately modeling the com-plex image characteristics with handcraft priors, and deep learning-based methods suffer from poor generalization ability. To alleviate these issues, this paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff), which restores the clean HSls from the product of two low-rank components, i.e., the re-duced image and the coefficient matrix. Specifically, the re-duced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled. The coefficient matrix can be effectively pre-estimated based on singular value decomposition (SVD) and rank-revealing QR (RRQR) factorization. Fur-thermore, a novel exponential noise schedule is proposed to accelerate the restoration process (about 5 x acceleration for denoising) with little performance decrease. Ex-tensive experimental results validate the superiority of our method in both performance and speed on a variety of HSI restoration tasks, including HSI denoising, noisy HSI super-resolution, and noisy HSI inpainting. The code is available at https://***/LiPang/HIRDiff.

关键词： Schedules TV Noise reduction Noise Superresolution Diffusion models Image restoration

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PACF: Prototype Augmented Compact Features for Improving Domain Adaptive Object Detection

arXiv

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arXiv 2025年

作者： Liu, Chenguang Feng, Yongchao Zhang, Yanan Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

In recent years, there has been significant advancement in object detection. However, applying off-the-shelf detectors to a new domain leads to significant performance drop, caused by the domain gap. These detectors exhibit higher-variance class-conditional distributions in the target domain than that in the source domain, along with mean shift. To address this problem, we propose the Prototype Augmented Compact Features (PACF) framework to regularize the distribution of intra-class features. Specifically, we provide an in-depth theoretical analysis on the lower bound of the target features-related likelihood and derive the prototype cross entropy loss to further calibrate the distribution of target RoI features. Furthermore, a mutual regularization strategy is designed to enable the linear and prototype-based classifiers to learn from each other, promoting feature compactness while enhancing discriminability. Thanks to this PACF framework, we have obtained a more compact cross-domain feature space, within which the variance of the target features’ class-conditional distributions has significantly decreased, and the class-mean shift between the two domains has also been further reduced. The results on different adaptation settings are state-of-the-art, which demonstrate the board applicability and effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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