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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Christian Witte Jens Behley Cyrill Stachniss Marvin Raaijmakers CARIAD SE Germany Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Spatial understanding of the semantics of the surroundings is a key capability needed by autonomous cars to enable safe driving decisions. Recently, purely vision-based solutions have gained increasing research interest. In particular, approaches extracting a bird's eye view (BEV) from multiple cameras have demonstrated great performance for spatial understanding. This paper addresses the dependency on learned positional encodings to correlate image and BEV feature map elements for transformer-based methods. We propose leveraging epipolar geometric constraints to model the relationship between cameras and the BEV by Epipolar Attention Fields. They are incorporated into the attention mechanism as a novel attribution term, serving as an alternative to learned positional encodings. Experiments show that our method EAFormer outperforms previous BEV approaches by 2% mIoU for map semantic segmentation and exhibits superior generalization capabilities compared to implicitly learning the camera configuration.

关键词： Computer vision Image coding Attention mechanisms Semantic segmentation Semantics Cameras Transformers Feature extraction Encoding Automobiles

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Nerve function restoration following targeted muscle reinnervation after varying delayed periods

引用

Neural Regeneration Research 2023年第12期18卷 2762-2766页

作者： Yuanheng Li Jiangping Huang Yuling Chen Shanshan Zhu Zhen Huang Lin Yang Guanglin Li Key Laboratory of Human-Machine Intelligence-Synergy Systems and Branch of Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong ProvinceChina Department of Rehabilitation Medicine Guangzhou University of Chinese MedicineGuangzhouGuangdong ProvinceChina Department of Rehabilitation Medicine Yibin Hospital of Traditional Chinese MedicineYibinSichuan ProvinceChina

Targeted muscle reinnervation has been proposed for reconstruction of neuromuscular function in ***,it is unknown whether performing delayed targeted muscle reinnervation after nerve injury will affect restoration of *** this rat nerve injury study,the median and musculocutaneous nerves of the forelimb were *** proximal median nerve stump was sutured to the distal musculocutaneous nerve stump immediately and 2 and 4 weeks after surgery to reinnervate the biceps *** targeted muscle reinnervation,intramuscular myoelectric signals from the biceps brachii were *** amplitude gradually increased with *** brachii myoelectric signals and muscle fiber morphology and grooming behavior did not significantly differ among rats subjected to delayed target muscle innervation for different *** muscle reinnervation delayed for 4 weeks can acquire the same nerve function restoration effect as that of immediate reinnervation.

关键词： biceps brachii delayed nerve repair delayed targeted muscle reinnervation functional reconstruction grooming test implanted electrode intramuscular myoelectric signals median nerve motor function nerve transfer

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SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions

arXiv

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arXiv 2025年

作者： Samavi, Sepehr Lem, Anthony Sato, Fumiaki Chen, Sirui Gu, Qiao Yano, Keijiro Schoellig, Angela P. Shkurti, Florian University of Toronto Robotics Institute Vector Institute for Artificial Intelligence Toronto Canada Munich Germany Konica Minolta Inc. Japan

To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have shown success in generating likely human trajectory predictions, integrating these stochastic models into a robot controller presents several challenges. The controller needs to account for interactive coupling between planned robot motion and human predictions while ensuring both predictions and robot actions are safe (i.e. collision-free). To address these challenges, we present a receding horizon crowd navigation method for single-robot multi-human environments. We first propose a diffusion model to generate joint trajectory predictions for all humans in the scene. We then incorporate these multi-modal predictions into a SICNav Bilevel MPC problem that simultaneously solves for a robot plan (upper-level) and acts as a safety filter to refine the predictions for non-collision (lower-level). Combining planning and prediction refinement into one bilevel problem ensures that the robot plan and human predictions are coupled. We validate the open-loop trajectory prediction performance of our diffusion model on the commonly used ETH/UCY benchmark and evaluate the closed-loop performance of our robot navigation method in simulation and extensive real-robot experiments demonstrating safe, efficient, and reactive robot motion. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic models

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Domain Adaptive Multitask Model for Object Detection in Foggy Weather Conditions

Domain Adaptive Multitask Model for Object Detection in Fogg...

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2023 China Automation Congress, CAC 2023

作者： Yang, Yawei Chen, Tao Zhou, Gang Cai, Yaping Wang, Jianji Tian, Zhiqiang College of Information Science and Engineering Xinjiang University Urumqi China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China School of Software Engineering Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9798350303759

Object detection in foggy weather conditions is a challenging problem, facing image degradation and feature space mismatch. A well-known method is to use image restoration methods to enhance degraded images before object detection. However, due to the limited availability of real-world datasets, these methods are based on synthetic datasets and have limited effectiveness in real-world scenarios. To address such issues, this paper proposes a domain adaptation-based multitask foggy weather object detection method, aiming to improve object detection performance in real-world foggy images. Firstly, we design an efficient Mini-AOD-Net as an image restoration network, which effectively enhances image clarity and preserves clean features for the detection network. Secondly, we introduce a domain adaptation module to address the object detection generalization problem in real-world scenarios. Experimental results demonstrate varying degrees of improvement in detection accuracy for both synthetic and real datasets using our proposed method. Furthermore, our approach can achieve a detection speed of 5.2 frames per second on an embedded system. © 2023 IEEE.

关键词： Image reconstruction

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FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering 41

FedRC: Tackling Diverse Distribution Shifts Challenge in Fed...

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41st International Conference on Machine Learning, ICML 2024

作者： Guo, Yongxin Tang, Xiaoying Lin, Tao School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Research Center for Industries of the Future Westlake University China School of Engineering Westlake University China

Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning system. Though recent research has focused on improving the optimization of FL when distribution shifts occur among clients, ensuring global performance when multiple types of distribution shifts occur simultaneously among clients-such as feature distribution shift, label distribution shift, and concept shift-remain under-explored. In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges. Through our research, we find that existing methods fail to address the clustering principle. Therefore, we propose a novel clustering algorithm framework, dubbed as FedRC, which adheres to our proposed clustering principle by incorporating a bi-level optimization problem and a novel objective function. Extensive experiments demonstrate that FedRC significantly outperforms other SOTA cluster-based FL methods. Our code is available at https://***/LINs-lab/FedRC. Copyright 2024 by the author(s)

关键词： Federated learning

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Invariance and Contraction in Geometrically Periodic Systems with Differential Inclusions

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IEEE Transactions on Automatic Control 2025年

作者： Qian, Chen Fang, Yongchun Research Center for Intelligent Robotics Zhejiang Lab Research Institute of Interdisciplinary Innovation Hangzhou311100 China College of Artificial Intelligence Institute of Robotics and Automatic Information Systems China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300353 China

The objective of this paper is to derive the essential invariance and contraction properties for the geometric periodic systems, which can be formulated as a category of differential inclusions, and primarily rendered in the phase coordinate, or the cycle coordinate. First, we introduce the geometric averaging method for this category of systems, and also analyze the accuracy of its averaging approximation. Specifically, we delve into the details of the geometrically periodic system through the tunnel of considering the convergence between the system and its geometrically averaging approximation. Under different corresponding conditions, the approximation on infinite time intervals can achieve certain accuracies, such that one can use the stability result of either the original system or the averaging system to deduce the stability of the other. After that, we employ the graphical stability to investigate the "pattern stability"with respect to the phase-based system. Finally, by virtue of the contraction analysis on the Finsler manifold, the idea of accentuating the periodic pattern incremental stability and convergence is nurtured in the phase-based differential inclusion system, and comes to its preliminary fruition in application to biomimetic mechanic robot control problem. © 1963-2012 IEEE.

关键词： System stability

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A Socially Mediating Robot to Assist Non-Verbal Children in an Inclusive Daycare Context 25

A Socially Mediating Robot to Assist Non-Verbal Children in ...

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Jan Ole Rixen Rafik Abdalla Victoria Coralie Kuch Nadja Scherer Barbara Bruno Kathrin Gerling HCI and Accessibility Karlsruhe Institute of Technology Karlsruhe Germany Socially Assistive Robotics with Artificial Intelligence Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Non-verbal children are often provided with non-verbal communication cards (NVCCs) to share their needs. However, in busy spaces that prioritize verbal communication, for example, a daycare, this may place them at a disadvantage in comparison with verbal peers. In this work, we introduce the concept of a Socially Mediating Robot (SMR), acting as a communicational intermediary that verbalizes NVCC input. Thereby, we want to explore whether non-verbal children can be empowered by giving them an additional way of expressing themselves in a dynamic, buzzing environment. In an exploratory interview study with six participants professionally working with children, we then provide first insights on the feasibility of our concept from the perspective of caregiving staff. Thereby, we lay the groundwork for evaluating the concept in an ongoing field study in an inclusive daycare context.

关键词： Human-robot interaction Assistive robots Interviews Robots

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High-Accuracy Pose Estimation for 3D Reconstruction Considering the Manipulator Motion Constraints with an Eye-in-Hand RGB-D Camera 42

High-Accuracy Pose Estimation for 3D Reconstruction Consider...

引用

42nd Chinese Control Conference, CCC 2023

作者： Zhou, Yanling Zhang, Xuebo Wang, Runhua Zhang, Shiyong Yuan, Mingxing Yuan, Jing Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China

ISBN: (纸本)9789887581543

Three-dimensional(3D) environmental reconstruction of the manipulator workspace is a prerequisite to enable autonomous collision-free path planning and subsequent task execution, wherein the positioning accuracy of the equipped sensor is one key issue to determine the performance of 3D reconstruction. In this paper, an eye-in-hand RGB-D camera is used to complete the manipulator workspace scanning and a novel high-accuracy pose estimation method for 3D reconstruction is proposed, which not only considers the geometric and photometric constraints constructed from the visual information used in traditional methods, but also considers the manipulator motion constraints since the positioning accuracy of the manipulator is usually high. Based on above three constraints, three error functions are constructed respectively and fused with different weights to form an integrated objective function. Then, the problem is converted into solving the least squares problem by the Gaussian Newton nonlinear iterative method, which involves the computation of Jacobian matrices and residuals of the three error functions. To this end, the mathematical derivation of their formulas considering the manipulator motion constraints are given in detail. By qualitative and quantitative experimental comparison with traditional methods, the proposed method can greatly improve the accuracy of pose estimation and 3D reconstruction, which always maintains the error within 3 cm, and show good robustness even in the challenging environments with weak texture or geometric features. (Supplemented video link: https://***/FkFTxssnWms) © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Manipulators

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Adaptive Neural Network Dynamic Surface Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles

Adaptive Neural Network Dynamic Surface Trajectory Tracking ...

引用

robotics and Computer Vision (ICRCV), International Conference on

作者： Guohong Jiang Meng Joo Er Huibin Gong Siqi Wang Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Aiming at the trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) with unknown dynamic model parameters and time-varying ocean disturbances, a dynamic surface adaptive control strategy based on radial basis function neural network is proposed to complete the desired three-dimensional trajectory tracking. Firstly, an output redefinition method is used to solve the underactuated problem. Secondly, a dynamic surface control method is used to overcome the “explosion of complexity” problem in traditional backstepping control, and complexity of the control law design is simplified. At the same time, a nonlinear damping term is added in control law to overcome unknown external disturbances. Finally, unknown dynamic model parameters are approximated by a neural network, and the neural network weights are estimated online by an adaptive law. The Lyapunov theory is used to prove that all signals of closed-loop system are semi-globally uniformly bounded, and effectiveness of the control law is verified by comparative simulations.

关键词： Damping Sea surface Autonomous underwater vehicles Adaptation models Trajectory tracking Computational modeling Explosions Complexity theory Vehicle dynamics Adaptive control

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Dual-Role AoI-based Incentive Mechanism for HD map Crowdsourcing

arXiv

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arXiv 2024年

作者： Ye, Wentao Liu, Bo Luo, Yuan Huang, Jianwei Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

A high-quality fresh high-definition (HD) map is vital in enhancing transportation efficiency and safety in autonomous driving. Vehicle-based crowdsourcing offers a promising approach for updating HD maps. However, recruiting crowdsourcing vehicles involves making the challenging tradeoff between the HD map freshness and recruitment cost. Existing studies on HD map crowdsourcing often (1) prioritize maximizing spatial coverage, and (2) overlook the dual role of crowdsourcing vehicles in HD maps, as vehicles serve both as contributors and customers of HD maps. This motivates us to propose the Dual-Role Age of Information (AoI) based Incentive Mechanism (DRAIM) to address these issues. DRAIM aims to achieve the company's tradeoff between freshness and recruitment cost. Copyright © 2024, The Authors. All rights reserved.

关键词： Crowdsourcing

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