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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Visual-Language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification 24

Visual-Language Collaborative Representation Network for Bro...

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32nd ACM international conference on Multimedia, MM 2024

作者： Guo, Qianyu Ren, Jieji Wang, Haofen Wu, Tianxing Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798400706868

Visual-language models based on CLIP have shown remarkable abilities in general few-shot image classification. However, their performance drops in specialized fields such as healthcare or agriculture, because CLIP's pre-training does not cover all category data. Existing methods excessively depend on the multi-modal information representation and alignment capabilities acquired from CLIP pre-training, which hinders accurate generalization to unfamiliar domains. To address this issue, this paper introduces a novel visual-language collaborative representation network (MCRNet), aiming at acquiring a generalized capability for collaborative fusion and representation of multi-modal information. Specifically, MCRNet learns to generate relational matrices from an information fusion perspective to acquire aligned multi-modal features. This relationship generation strategy is category-agnostic, so it can be generalized to new domains. A class-adaptive fine-tuning inference technique is also introduced to help MCRNet efficiently learn alignment knowledge for new categories using limited data. Additionally, the paper establishes a new broad-domain few-shot image classification benchmark containing seven evaluation datasets from five domains. Comparative experiments demonstrate that MCRNet outperforms current state-of-the-art models, achieving an average improvement of 13.06% and 13.73% in the 1-shot and 5-shot settings, highlighting the superior performance and applicability of MCRNet across various domains. © 2024 ACM.

关键词： Visual languages

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo University of Science and Technology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences Institute of Automation Beijing100190 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. © 2022 IEEE.

关键词： Chemical detection

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CPA-Unet: A Solution for Left Ventricle Segmentation from Magnetic Resonance Images

CPA-Unet: A Solution for Left Ventricle Segmentation from Ma...

引用

2023 international conference on System Science and Engineering, ICSSE 2023

作者： Vu, Ngoc-Tu Pham, Viet-Tien Pham, Van-Truong Tran, Thi-Thao Hanoi University of Science and Technology School of Electrical and Electronic Engineering Department of Automation Engineering Viet Nam

ISBN: (纸本)9798350322941

Medical image segmentation is a crucial first step in the development of healthcare and rehabilitation systems, particularly for the identification and planning of cardiovascular issues. In recent years, convolutional neural networks (CNNs) have produced outstanding results on a number of medical image segmentation tasks. Particularly the U-shaped architecture, also known as U-Net, has been extremely successful and set the de facto standard. U-Net often demonstrates difficulties in clearly expressing long-range dependency, nevertheless, as a result of the innate locality of convolution operations. In this study, we propose a new neural network architecture, namely CPA-Unet for problems involving cardiac image segmentation. The CPA-Unet model, which employs cutting-edge Deep Learning methods, more successfully provides better extraction of features for the segmentation of desired segmented objects, whereas earlier models did not contribute much because they ignored the details of the channel, spatial, and contextualization on big datasets. Our experiments upon this Sunnybrook Cardiac dataset and the ACDC dataset show that CPA-Unet outperforms other modern models in terms of the Dice coefficient and IoU metric, highlighting it's own applicability for biomedical image segmentation solutions. © 2023 IEEE.

关键词： Convolution

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GraspAda: Deep Grasp Adaptation through Domain Transfer

GraspAda: Deep Grasp Adaptation through Domain Transfer

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yiting Chen Junnan Jiang Ruiqi Lei Yasemin Bekiroglu Fei Chen Miao Li Institute of Technological Sciences Wuhan University Wuhan China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electrical Engineering Chalmers University of Technology Goteborg Sweden Department of Computer Science University College London UK Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong China

Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.

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Algorithms in Surgical Action Recognition: A Survey

Algorithms in Surgical Action Recognition: A Survey

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IEEE international conference on Real-time Computing and robotics (RCAR)

作者： Gui-Bin Bian Yaqin Peng Li Zhang Jun Li Zhen Li Chinese Academy of Sciences Institute of Automation Beijing China Chinese Academy of Sciences Fujian Institute of Research on the Structure of Matter China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Machine learning and medical image processing technology continue to promote the advancement of scene perception method. Among the field of scene perception, surgical action recognition is finer-grained and conductive to surgical guidance and evaluation, promoting the realization of automatic surgical robot. In this work, an overall review of surgical action recognition is provided, including the methods, datasets, metrics, and prospects. From the perspective of labeling degree of datasets, the methods are categorized: supervised learning, weakly-supervised or unsupervised learning, and reinforcement learning. Correspondingly, their algorithms, performances and contributions are concluded. Then, the commonly used datasets and metrics in surgical action recognition are summarized. Finally, the prospect is discussed. This survey highlights the relevant corner stones and latest researches in the field, covers the widely used datasets and metrics, offers valuable insight into the prospects, and aims to gain more attention of researchers to surgical action recognition.

关键词： Measurement Surveys Medical robotics Reviews Supervised learning Reinforcement learning Software

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Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives

Goal-Conditioned End-to-End Visuomotor Control for Versatile...

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IEEE international conference on robotics and automation (ICRA)

作者： Groth, Oliver Hung, Chia-Man Vedaldi, Andrea Posner, Ingmar Univ Oxford Dept Engn Sci Oxford England

ISBN: (纸本)9781728190778

Visuomotor control (VMC) is an effective means of achieving bask manipulation tasks such as pushing or pick-and-place from raw images. Conditioning VMC on desired goal states is a promising way of achieving versatile skill primitives. However, common conditioning schemes either rely on task-specific fine tuning - e.g. using one-shot imitation learning (IL) - or on sampling approaches using a forward model of scene dynamics i.e. model-predictive control (MPC), leaving deployability and planning horizon severely limited. In this paper we propose a conditioning scheme which avoids these pitfalls by learning the controller and its conditioning in an end-to-end manner. Our model predicts complex action sequences based directly on a dynamic image representation of the robot motion and the distance to a given target observation. In contrast to related works, this enables our approach to efficiently perform complex manipulation tasks from raw image observations without predefined control primitives or test time demonstrations. We report significant improvements in task success over representative MPC and IL baselines. We also demonstrate our model's generalisation capabilities in challenging, unseen tasks featuring visual noise, cluttered scenes and unseen object geometries.

关键词： Geometry Robot motion Visualization Dynamics Predictive models Image representation Planning

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Analyzing Radar Cross Section signatures at automotive microwave radar 3

Analyzing Radar Cross Section signatures at automotive micro...

引用

3rd international Computing Imaging conference, CITA 2023

作者： Wang, Zizheng Qi, Feng Wu, Hongming Xidian University Xi’an710126 China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China College of information science anf engineering Northeastern University Liaoning Province Shenyang110819 China

ISBN: (纸本)9781510671409

With the increasing demand for autonomous vehicles, the reliable detection and recognition of objects have become paramount. Millimeter-wave radar systems operating at frequencies such as 94GHz offer several advantages, including high resolution, precise range measurement, and resilience to adverse weather conditions. However, accurately characterizing the RCS of objects at this frequency range is essential to optimize the performance of these radar systems. In this work, we present Radar Cross Section (RCS) measurements of various objects commonly encountered on the road such as the metal balls, the bricks. The acquired findings illuminate the radar signal fluctuations demonstrated by different entities at a frequency of 94GHz. This knowledge serves as a foundation for developing robust algorithms and signal processing techniques that enhance the object detection, classification, and tracking capabilities of radar-based autonomous driving systems. Moreover, our research provides a valuable dataset of RCS measurements for different objects at 94GHz, which can serve as a benchmark for future studies in the field of radar system design and testing. The availability of such a dataset facilitates the development and validation of radar system models, the evaluation of sensor fusion approaches, and the comparison of performance across different radar hardware and signal processing algorithms. The results contribute to the advancement of radar-based autonomous driving systems by providing crucial insights into the scattering characteristics of objects at millimeter-wave frequencies. The findings have practical implications for optimizing the design, testing, and performance of radar systems, ultimately enhancing the safety and efficiency of autonomous vehicles in real-world scenarios. © 2023 SPIE.

关键词： Millimeter waves

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Accurate and Real-time LiDAR Point Cloud Forecasting for Autonomous Driving 24

Accurate and Real-time LiDAR Point Cloud Forecasting for Aut...

引用

15th Indian conference on Computer Vision Graphics and Image Processing

作者： Dasgupta, Soham Aphale, Kshitij Pal, Kaustab Sharma, Avinash IIT Jodhpur Jodhpur Rajasthan India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Point cloud forecasting is a crucial task for the success of motion planning and state estimation problems. However, due to its complex nature and challenges in integrating with existing architectures, it remains an extremely difficult and interesting problem. LiDAR-based 3D sensing is one of the key components in modern autonomous driving systems and leads to scalability challenges as it yields large-scale point cloud data at a high frame rate. Range image representation of point cloud offers a compact representation of LiDAR data which enables applying powerful convolution network architectures for predicting future range images (and thereby point clouds). In this paper, we use range image representation and propose two different non-recurrent deep network models for point cloud forecasting. More specifically, we predict future point clouds from past observed point clouds by introducing a spatio temporal convolution (STC) block in the latent space of range images, thereby avoiding the use of RNNs to achieve much faster inference. The STC has two variants that use the temporal attention and Inception-Net blocks, respectively. We perform experiments on two publicly available datasets, namely KITTI and Nuscenes and report superior quantitative and qualitative results in comparison to SOTA methods while offering compact model with faster inference speeds.

关键词： Range Sensing 3D computer vision robotics

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Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input

Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation fro...

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IEEE international conference on robotics and automation (ICRA)

作者： Yiye Chen Yunzhi Lin Ruinian Xu Patricio A. Vela School of Electrical and Computer Engineering Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA

The success of 6-DoF grasp learning with point cloud input is tempered by the computational costs resulting from their unordered nature and pre-processing needs for reducing the point cloud to a manageable size. These properties lead to failure on small objects with low point cloud cardinality. Instead of point clouds, this manuscript explores grasp generation directly from the RGB-D image input. The approach, called Keypoint-GraspNet (KGN), operates in perception space by detecting projected gripper keypoints in the image, then recovering their SE(3) poses with a $\mathrm{P}n\mathrm{P}$ algorithm. Training of the network involves a synthetic dataset derived from primitive shape objects with known continuous grasp families. Trained with only single-object synthetic data, Keypoint-GraspNet achieves superior result on our single-object dataset, comparable performance with state-of-art baselines on a multi-object test set, and outperforms the most competitive baseline on small objects. Keypoint-GraspNet is more than 3x faster than tested point cloud methods. Robot experiments show high success rate, demonstrating KGN's practical potential.

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Disturbance Observation and Control of Operational Unmanned Aerial Vehicles

Disturbance Observation and Control of Operational Unmanned ...

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IEEE international conference on Unmanned Systems (ICUS)

作者： Guanyu Yao Liying Yang Yanchun Chang Yuqing He School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

UAVs are used widely in all works and life, and their operation forms and application scenarios are more complex than ever before. In order to deal with various disturbances faced by operational UAVs in application scenarios and achieve stable control, a disturbance observation and compensation structure combining linear extended state observer and sliding mode controller is proposed to achieve stable control of operational UAVs. Firstly, the typical operational UAV is taken as the research object, and the kinematics and dynamics models are established. Then, a linear extended state observer is designed to estimate the environment and body disturbances, and the sliding mode control method is combined to control the system. Finally, the effectiveness of the designed control method is verified through simulation.

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