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arXiv 2024年

作者： Lin, Qinliang Luo, Cheng Niu, Zenghao He, Xilin Xie, Weicheng Hou, Yuanbo Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China WAVES Research Group Ghent University Belgium University of Leicester United Kingdom

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://***/LinQinLiang/DeCoWA. © 2024, CC BY-NC-SA.

关键词： Semantics

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PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION FOR UNSUPERVISED DOMAIN ADAPTATION

PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yong Li, Desheng Lu, Yuwu Gao, Can Wang, Wenjing Lu, Jianglin College of Computer Science and Software Engineering Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781665438643

Unsupervised domain adaptation (UDA) aims to transfer knowledge between different domains. Most of the existing UDA methods try to align the conditional distribution between the source and target domains by utilizing the information of pseudo labels induced from the target domain. To tackle the negative transfer caused by inaccurate pseudo labels, we propose a novel UDA method named progressive distribution alignment based on label correction (PDALC). Specifically, PDALC uses the class discriminative information to perform subspace learning to obtain the domain invariance subspace. Furthermore, a new mechanism of pseudo label correction is introduced to measure the reliability of pseudo labels and utmostly correct the inaccurate pseudo labels. By combining subspace learning with label correction, the performance of PDALC can be continuously improved, which in turn reduces the generation of inaccurate pseudo labels. The experimental results show that the proposed method outperforms the state-of-the-art UDA methods. © 2021 IEEE

关键词： computer vision

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Flying Welding Path Planning Based on Improved Two-chromosome Genetic Algorithm

Flying Welding Path Planning Based on Improved Two-chromosom...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wenlong Zhang Tianjiang Zheng Kunpeng Yan Hongling Tian Hongyuan Lian Qiping Zhang Faculty of Mechanical Engineering and Mechanics Ningbo University Ningbo China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Aiming to address the issues of moving speed and overall machining efficiency of a robot arm in path planning for laser flying welding, a method is proposed. This method involves using the non-central point of the trajectory to be welded as the welding position point and is further extended to the generalized traveling salesman problem. Through the analysis of the optimization model, the two-chromosome genetic algorithm is utilized to solve the path planning. Based on this, path intersection elimination and neighbor node replacement are carried out for every iteration in order to enhance the quality of the best solution. Finally, the path planning based on the general genetic algorithm for central welding position points, and the path planning based on the two-chromosome genetic algorithm and the improved two-chromosome genetic algorithm for non-central welding position points are simulated. The results show that the improved two-chromosome genetic algorithm can calculate the optimal solution of higher quality and a more reasonable machining path for non-central point welding positions.

关键词： Industrial electronics Welding Optimization models Machining Traveling salesman problems Manipulators Laser modes

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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MechTac: A Multifunctional Tendon-Linked Optical Tactile Sensor for In/Out-the-Field-of-View Perception with Deep Learning

MechTac: A Multifunctional Tendon-Linked Optical Tactile Sen...

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Annual Conference of Industrial Electronics Society

作者： Zhenyu Lu Tianqi Yue Zhou Zhao Weiyong Si Ning Wang Chenguang Yang Bristol Robotics Laboratory University of the West of England Bristol UK Department of Engineering Mathematics University of Bristol Bristol UK School of Computer Science Central China Normal University Wuhan China

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlooked. In this paper, we present a new tendon-linked tactile sensor, MechTac, to achieve perceptions inside and outside the field of view. The MechTac is an evolution of the typical TacTip sensor with the following two advantages. 1) The ability to provide perception outside the field of view. This is achieved by using a network of braided tendons to transfer deformation from the blind perception regions (TacSide) to the visual areas (TacTip). 2) The tactility of the TacSide and TacTip is reflected by the movements of multiple papillae pins and visible markers on the pin tips on the inner surface of the TacTip. The pins and markers are differentially sensitive to various touch features, which is similar to the differentiated perceptual ability of humans. TacTip is more sensitive to small touches, corresponding to the fingertip, while the TacSide is less sensitive but has a larger perceptual area, corresponding to the middle part of the finger. Moreover, we propose a new deep learning method to decompose the mixed information affected by the TacSide and the TacTip. A modified DenseNet121 was specifically designed for object perception at the TacTip and localization at the TacSide. The experimental results show that prediction accuracy reaches about 98% for object perception or localization and over 99% for the case requiring two functions.

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

arXiv

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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Ding, Menglong Zhu, Shiqiang Liang, Yiming Bai, Yunhe Xie, Keren Wan, Minhong Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665481090

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration. © 2022 IEEE.

关键词： Conceptual design

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Ahmed, Imran Ahmad, Misbah Siddiqi, Muftooh Ur Rehman Chehri, Abdellah Jeon, Gwangill Anglia Ruskin University School of Computing and Information Science CambridgeCB1 1PT United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory BristolBS16 1QY United Kingdom Hartpury University Department of Animal and Agriculture GloucesterGL19 3BE United Kingdom Aston University College of Engineering and Physical Sciences BirminghamB4 7ET United Kingdom Royal Military College of Canada Department of Mathematics and Computer Science KingstonONK7K 7B4 Canada Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology. © 2014 IEEE.

关键词： Artificial intelligence (AI) autonomous vehicles deep leaning edge computing Internet of Vehicles (IoV)

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Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning

arXiv

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arXiv 2023年

作者： Shi, Haojie Zhu, Qingxu Han, Lei Chi, Wanchao Li, Tingguang Meng, Max Q.-H. Tencent Robotics X China The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive and exteroceptive states with a parameterized trajectory generator for quadruped robots to traverse rough terrains. Our key idea is to use a DNN policy that can modify the parameters of the trajectory generator, such as foot height and frequency, to adapt to different terrains. To encourage the robot to step on safe regions and save energy consumption, we propose foot terrain reward and lifting foot height reward, respectively. By incorporating these rewards, our method can learn a safer and more efficient terrain-aware locomotion policy that can move a quadruped robot flexibly in any direction. To evaluate the effectiveness of our approach, we conduct simulation experiments on challenging terrains, including stairs, stepping stones, and poles. The simulation results demonstrate that our approach can successfully direct the robot to traverse such tough terrains in any direction. Furthermore, we validate our method on a real legged robot, which learns to traverse stepping stones with gaps over 25.5cm. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Toward Fine Contact Interactions: Learning to Control Normal...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jinda Cui Jiawei Xu David Saldana Jeff Trinkle Lehigh AIRLab Honda Research Institute USA San Jose CA USA Department of Computer Science and Engineering Autonomous and Intelligent Robotics Laboratory (AIRLab) Lehigh University Bethleham PA USA

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, i.e., controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.

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