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2023 IEEE Symposium Series on Computational intelligence, SSCI 2023

作者： Gao, Meng Liu, Xiao-Fang Zhan, Zhi-Hui Zhang, Jun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

ISBN: (纸本)9781665430654

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time. © 2023 IEEE.

关键词： dynamic optimization evolutionary computation support vector machine time-linkage

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3D Video Object Detection with Learnable Object-Centric Global Optimization

3D Video Object Detection with Learnable Object-Centric Glob...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiawei He Yuntao Chen Naiyan Wang Zhaoxiang Zhang CRIPAC Institute of Automation Chinese Academy of Sciences (CASIA) School of Artificial Intelligence University of Chinese Academy of Sciences (UCAS) Centre for Artificial Intelligence and Robotics HKISI_CAS TuSimple

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://***/jiaweihe1996/BA-Det.

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PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration

arXiv

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arXiv 2024年

作者： Yao, Runzhao Du, Shaoyi Cui, Wenting Tang, Canhui Yang, Chengwu National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Learning rotation-invariant distinctive features is a fundamental requirement for point cloud registration. Existing methods often use rotation-sensitive networks to extract features, while employing rotation augmentation to learn an approximate invariant mapping rudely. This makes networks fragile to rotations, overweight, and hinders the distinctiveness of features. To tackle these problems, we propose a novel position-aware rotation-equivariant network, for efficient, light-weighted, and robust registration. The network can provide a strong model inductive bias to learn rotation-equivariant/invariant features, thus addressing the aforementioned limitations. To further improve the distinctiveness of descriptors, we propose a position-aware convolution, which can better learn spatial information of local structures. Moreover, we also propose a feature-based hypothesis proposer. It leverages rotation-equivariant features that encode fine-grained structure orientations to generate reliable model hypotheses. Each correspondence can generate a hypothesis, thus it is more efficient than classic estimators that require multiple reliable correspondences. Accordingly, a contrastive rotation loss is presented to enhance the robustness of rotation-equivariant features against data degradation. Extensive experiments on indoor and outdoor datasets demonstrate that our method significantly outperforms the SOTA methods in terms of registration recall while being lightweight and keeping a fast speed. Moreover, experiments on rotated datasets demonstrate its robustness against rotation variations. Code is available at https://***/yaorz97/PARENet. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving

PhysORD: A Neuro-Symbolic Approach for Physics-infused Motio...

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

作者： Zhipeng Zhao Bowen Li Yi Du Taimeng Fu Chen Wang Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab Institute for Artificial Intelligence and Data Science University at Buffalo Buffalo NY USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. It outperforms existing methods both in accuracy and efficiency and demonstrates data-efficient learning and generalization ability in long-term prediction.

关键词： Bridges Uncertainty Accuracy Neural networks Merging Predictive models Mathematical models Dynamical systems Intelligent robots

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Design and analysis of a soft bellow-based robot for dysphagia rehabilitation 30

Design and analysis of a soft bellow-based robot for dysphag...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Guo, Linlin Dang, Yu Wang, Yue Wu, Jinqiao Yu, Yang Wu, Jialing Han, Jianda College of Artificial Intelligence Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute Nankai University Shenzhen518083 China Department of Rehabilitation Medicine Tianjin Huanhu Hospital Tianjin300350 China Department of Neurology Tianjin Huanhu Hospital Tianjin300350 China

ISBN: (纸本)9798350391916

As the population ages, the incidence of diseases such as stroke is increasing, leading to a growing number of patients with dysphagia or difficulty swallowing. Dysphagia directly affects the hydration and nutritional intake, potentially leading to serious complications such as aspiration pneumonia, severely threatening patients' health and quality of life. Currently, the rehabilitation of swallowing function mainly relies on therapists to conduct swallowing training for patients. However, the high incidence and long duration of swallowing rehabilitation training pose a burden on the patients and medical resources. There remains a gap in the robotic devices for swallowing rehabilitation. To address this gap, this paper proposes a soft bellow-shaped robot for dysphagia rehabilitation. One mode of the rehabilitation training, where the therapist manipulates the throat to move horizontally, is selected to mimic. This robot simulates the force and displacement applied by the therapist's fingertip during dysphagia rehabilitation. First, swallowing training data were collected and analyzed to understand the variations in displacement and force applied by therapists' fingertips on the throat. Based on this analysis, the technical requirements for the swallowing rehabilitation robot were summarized. Following these requirements, we designed a physical prototype of the pneumatic bellow-shaped robot. The robot simulates the fingertip movement on both sides of the throat through the linear elongation and contraction of the bellows. The performance of the soft bellows was then characterized, verifying that its output force and displacement meet the technical requirements. This characterization lays a foundation for the modeling and control of the displacement and force of the robot. © 2024 IEEE.

关键词： Bellows

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PTZ Camera Control on Robot Telemedicine Service for Infectious Diseases

PTZ Camera Control on Robot Telemedicine Service for Infecti...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Seongah Park Youngsun Kwon Yoonseob Lim Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul Korea

Attentions for medical assistance services to isolated patients have increased due to recent infectious diseases. In the isolation case, telemedicine service is valuable because of no direct contact between infected patients and medical staff. This work proposes a pan-tilt-zoom (PTZ) camera control system that provides convenience for medical staff during telemedicine. Our automatic camera controller tracks the region while maintaining the best viewpoint when medical staff selects a region of interest, e.g., a patient’s symptoms or wounds. In order to develop the system, several attributes of object trackers suitable for the proposed control system were explored. Based on the evaluations of object trackers, we found that the scale-variance property of object trackers is significant to our PTZ controller. As a result, the proposed approach using a scale-variant object tracker can control the pan, tilt, and zoom to support a convenient telemedicine service without annoying manual viewpoint control.

关键词： Target tracking Automation Infectious diseases Telemedicine Robot vision systems Manuals Wounds

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P-Norm Based Subband Adaptive Filtering Algorithm 6

P-Norm Based Subband Adaptive Filtering Algorithm

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Ye, Jianhong Yu, Yi Cai, Qiangming Yu, Tao Chen, Badong School of Information Engineering Southwest University of Science and Technology Mianyang621010 China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Institute of Artificial Intelligence and Robotics Xi'anJiaotong University Shaanxi Province Xi'an710049 China

ISBN: (纸本)9798350399059

The normalized subband adaptive filtering algorithm can improve the convergence rate of the normalized least mean square algorithm when dealing with the correlated input signals, but it is plagued by a slow convergence issue in the stable noise. For that reason, the normalized subband p-norm (NSPN) algorithm based on the mean p-power error criterion is proposed in this paper, which shows a fast convergence rate in the α-stable noise. Moreover, by taking advantage of the tap-weights feedback-based convex combination (TFC) scheme, we propose the TFC based NSPN algorithm, which further reaches low steady-state misadjustment under fast convergence. Simulation results have confirmed the superior performance of the proposed algorithms in both system identification and acoustic echo cancellation scenarios. © 2023 IEEE.

关键词： Adaptive filtering

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Design and Calibration of Dual Sensor: Towards Simultaneous Instrument Tracking and Deformable Shape Sensing in Computer-Integrated Surgery

Design and Calibration of Dual Sensor: Towards Simultaneous ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sangseo Jeon Khaing Thandar Hnin Yong Gi Jung Sunghwan Lim Center for Healthcare Robotics Artificial Intelligence and Robotics Institute Korea Institute of Science and Technology Seoul Korea Department of Otorhinolaryngology - Head and Neck Surgery Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

We introduce a novel integration of vision sensors in the form of a dual sensor system. The dual sensor system combines three distinct units: an optical localizer and two RGB-D sensors. This system is uniquely capable of concurrently providing real-time 3D shape information for deformable objects and accurate pose tracking data for medical instruments. This capability positions the dual sensor with immense potential for application in image-guided surgical navigation, robotic systems, and robot-assisted automation for surgical procedures involving deformable target objects. This paper provides an overview of the design, fabrication and a one-time calibration methodology of the dual sensor. An experiment was performed to assess the calibration accuracy of the dual sensor. Experimental results indicated that the dual sensor system delivers a level of accuracy that renders it suitable for deployment within the envisioned scope of *** relevance— The presented dual sensor provides real-time shape scanning of intra-operatively deforming objects and instrument tracking capabilities simultaneously, contributing to successful outcomes of diverse computer-integrated surgeries dealing with non-rigid surgical targets such as breast, face, and incised skin area

关键词： Accuracy Target tracking Three-dimensional displays Shape Surgery Robot sensing systems Sensor systems Real-time systems Calibration Optical sensors

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Fairly Adaptive Negative Sampling for Recommendations 23

Fairly Adaptive Negative Sampling for Recommendations

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2023 World Wide Web Conference, WWW 2023

作者： Chen, Xiao Fan, Wenqi Chen, Jingfan Liu, Haochen Liu, Zitao Zhang, Zhaoxiang Li, Qing The Hong Kong Polytechnic University Center for Artificial Intelligence and Robotics HKISI_CAS Hong Kong The Hong Kong Polytechnic University Hong Kong Michigan State University United States Guangdong Institute of Smart Education Jinan University China

ISBN: (纸本)9781450394161

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model's unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method's superiority in group fairness enhancement and fairness-utility tradeoff. © 2023 Owner/Author.

关键词： Recommender systems

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Robotic Assistance for Standardized Compression Ultrasound Elastography

Robotic Assistance for Standardized Compression Ultrasound E...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Wang, Chenwei Gao, Qian Zhang, Danyang Wang, Ziya Sun, Zhenglong The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen518172 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen518060 China

ISBN: (纸本)9798350372601

2D ultrasound elastography is crucial for diagnosing superficial organ diseases, favored for its simplicity and affordability. However, it is limited to qualitative assessments, leading to potential diagnostic inconsistencies due to the dependence on clinician experience and operator skill. Integrating robotic technology into compression-based elastography offers a promising approach for achieving standardized and quantitative diagnostics. This paper presents a robotic-technology-based quantitative measurement scheme for compression-based elastography, focusing on utilizing the medical robotic platform;this study quantitatively performs compression elastography using ultrasound image features to determine the Young's modulus of tissues. The scheme includes refined measurement methods for better accuracy and robustness, such as adjusting the contact area. The proposed method is validated on self-made phantoms. The results indicate that within the typical range of low Young's modulus values for superficial organs, this method can accurately measure the Young's modulus of embedded tissues and preliminarily verify that there is a good linear relationship between the Young's modulus and some ultrasound image feature parameters within a certain range of Young's modulus values. © 2024 IEEE.

关键词： Ultrasonic imaging

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