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Representation of Human Arm Dynamic Intents with An Electrical-Impedance-Tomography (EIT)-Driven Musculoskeletal Model for Human-Robot Interaction

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IEEE Transactions on robotics 2025年 41卷 3278-3296页

作者： Zheng, Enhao Liu, Xiaodong Xu, Chenfeng Zhou, Zhihao Wang, Qining State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute for Artificial Intelligence Peking University Beijing100871 China Faculty of Engineering School of Advanced Manufacturing and Robotics Peking University The Institute for Artificial Intelligence Beijing100871 China

Representing human arm dynamic intent is essential for efective human-robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses signifcant challenges. This study introduces an EIT-driven musculoskeletal model which integrates an EIT sensing system with methods for muscle identifcation, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multi-DoF wrist kinematics estimation under varying contraction intensities, arm endpoint stifness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks. © 2025 IEEE. All rights reserved.

关键词： Muscle

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Intelligent Electric Vehicles

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2025年

作者： Shaoshan Liu

ISBN: (数字)9781468608502;9781468608519

ISBN: (纸本)9781468608496

Embark on a journey into the future of transportation with Intelligent Electric Vehicles. This comprehensive guide demystifies complex concepts, offering a roadmap to harness the monetization opportunities within the thriving IEV ecosystem. From management strategies to cutting-edge technology, this book provides a holistic perspective on the IEV industry. Explore real-world case studies, learn about emerging trends like cockpit intelligence and connected vehicles, and discover how to navigate the challenges and opportunities of this transformative space. Key Features: • Interdisciplinary approach: Bridges the gap between management and technology. • Real-world case studies: Grounds theoretical knowledge in practical applications. • Future-focused insights: Prepares readers for the next wave of innovations. • Monetization roadmap: Offers strategic advice for capitalizing on IEV advancements. Whether you"re an automotive industry professional, technology enthusiast, or investor, Intelligent Electric Vehicles is your essential guide to understanding and succeeding in this exciting new era of transportation.

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Human-like dexterous manipulation for anthropomorphic five-fingered hands:A review

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Biomimetic intelligence & robotics 2025年第1期5卷 35-57页

作者： Yayu Huang Dongxuan Fan Haonan Duan Dashun Yan Wen Qi Jia Sun Qian Liu Peng Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and InnovationChinese Academy of SciencesHong Kong 999077China

Humans excel at dexterous manipulation;however,achieving human-level dexterity remains a significant challenge for *** breakthroughs in the design of anthropomorphic robotic hands,as well as advancements in visual and tactile perception,have demonstrated significant advantages in addressing this ***,coping with the inevitable uncertainty caused by unstructured and dynamic environments in human-like dexterous manipulation tasks,especially for anthropomorphic five-fingered hands,remains an open *** this paper,we present a focused review of human-like dexterous manipulation for anthropomorphic five-fingered *** begin by defining human-like dexterity and outlining the tasks associated with human-like robot dexterous ***,we delve into anthropomorphism and anthropomorphic five-fingered hands,covering definitions,robotic design,and evaluation ***,we review the learning methods for achieving human-like dexterity in anthropomorphic five-fingered hands,including imitation learning,reinforcement learning and their ***,we discuss the existing challenges and propose future research *** review aims to stimulate interest in scientific research and future applications.

关键词： Anthropomorphic five-fingered hands Human-like dexterous manipulation Robot learning Review

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Visual-Linguistic Feature Alignment with Semantic and Kinematic Guidance for Referring Multi-Object Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3034-3044页

作者： Li, Yizhe Zhou, Sanping Qin, Zheng Wang, Le 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 Shaanxi Xi'an710049 China

Referring Multi-Object Tracking (RMOT) aims to dynamically track an arbitrary number of referred targets in a video sequence according to the language expression. Previous methods mainly focus on cross-modal fusion at the feature level with designed structures. However, the insufficient visual-linguistic alignment is prone to causing visual-linguistic mismatches, leading to some targets being tracked but not correctly referred especially when facing the language expression with complex semantics or motion descriptions. To this end, we propose to conduct visual-linguistic alignment with semantic and kinematic guidance to effectively align the visual features with more diverse language expressions. In this paper, we put forward a novel end-to-end RMOT framework SKTrack, which follows the transformer-based architecture with a Language-Guided Decoder (LGD) and a Motion-Aware Aggregator (MAA). In particular, the LGD performs deep semantic interaction layer-by-layer in a single frame to enhance the alignment ability of the model, while the MAA conducts temporal feature fusion and alignment across multiple frames to enable the alignment between visual targets and language expression with motion descriptions. Extensive experiments on the Refer-KITTI and Refer-KITTI-v2 demonstrate that SKTrack achieves state-of-the-art performance and verify the effectiveness of our framework and its components. © 1999-2012 IEEE.

关键词： Semantics

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Towards Autonomous Cardiac Ultrasound Scanning: Combining Physician Expertise and Machine intelligence

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical robotics and Bionics 2025年第2期7卷 782-792页

作者： Hao, Mingrui Zhang, Pengcheng Hou, Xilong Gu, Xiaolin Zhou, Xiao-Hu Hou, Zeng-Guang Chen, Chen Wang, Shuangyi Institute of Automation Chinese Academy of Sciences National Key Laboratory for Multi-Modal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100043 China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Hong Kong Lingshu Medical Company Suzhou215123 China

Echocardiography serves as a prevalent modality for both heart disease diagnosis and procedural guidance in medical applications. Nevertheless, the conventional echocardiography examination heavily relies on the manual dexterity of the sonographer, leading to the suboptimal repeatability. Despite the extensive exploration of robot-assisted ultrasound systems, achieving a heightened level of automation in examinations and enhancing the practicality of these robotic platforms for primary utilization remain formidable challenges within the field. In this study, we introduce an innovative automatic acquisition method for cardiac views using a novel ultrasound robot. The method is designed to autonomously traverse and scan target positions and angular ranges to search and identify the target cardiac views. First, the target positions and angular ranges were derived from a professional sonographer's practice on 14 cases. Then, an automatic traversal scanning method is designed integrating visual guidance, human-machine collaboration, and path planning within the framework of a novel parallel mechanism-based ultrasound robot. Finally, we explore deep metric learning to search for target ultrasound images in the traversed ultrasound video. Experiments on the test set to evaluate the target ultrasound view searching algorithm achieved a mAP of 98.8% and a Rank-1 accuracy of 98.23%. Our method has been successfully validated by data from five subjects, achieving the acquisition of standard parasternal long-axis and short-axis cardiac views essential for diagnosis, demonstrating the effectiveness of the proposed method. © 2018 IEEE.

关键词： Motion planning

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Few-Shot Object Detection via Dual-Domain Feature Fusion and Patch-Level Attention

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1237-1250页

作者： Guangli Ren Jierui Liu Mengyao Wang Peiyu Guan Zhiqiang Cao Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics Peking UniversityBeijing 100871China

Few-shot object detection receives much attention with the ability to detect novel class objects using limited annotated *** transfer learning-based solution becomes popular due to its simple training with good accuracy,however,it is still challenging to enrich the feature diversity during the training *** fine-grained features are also insufficient for novel class *** deal with the problems,this paper proposes a novel few-shot object detection method based on dual-domain feature fusion and patch-level *** original base domain,an elementary domain with more category-agnostic features is superposed to construct a two-stream backbone,which benefits to enrich the feature *** better integrate various features,a dual-domain feature fusion is designed,where the feature pairs with the same size are complementarily fused to extract more discriminative ***,a patch-wise feature refinement termed as patch-level attention is presented to mine internal relations among the patches,which enhances the adaptability to novel *** addition,a weighted classification loss is given to assist the fine-tuning of the classifier by combining extra features from FPN of the base training *** this way,the few-shot detection quality to novel class objects is *** on PASCAL VOC and MS COCO datasets verify the effectiveness of the method.

关键词： few-shot object detection dual-domain feature fusion patch-level attention

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Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation

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IEEE robotics and Automation Letters 2025年第6期10卷 6256-6263页

作者： Dawood, Murad Pan, Sicong Dengler, Nils Zhou, Siqi Schoellig, Angela P. Bennewitz, Maren University of Bonn Humanoid Robots Lab Bonn53113 Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn53113 Germany Technical University of Munich Learning Systems and Robotics Lab Munchen80333 Germany

In this letter, we address the problem of behavior-based cooperative navigation of mobile robots usingsafe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training. © 2025 IEEE.

关键词： Adversarial machine learning

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Event-enhanced synthetic aperture imaging

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Science China(Information Sciences) 2025年第3期68卷 388-390页

作者： Siqi LI Shaoyi DU Jun-Hai YONG Yue GAO Beijing National Research Center for Information Science and Technology School of SoftwareTsinghua University Institute for Brain and Cognitive Sciences Beijing Laboratory of Brain and Cognitive IntelligenceTsinghua University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and ApplicationsInstitute of Artificial Intelligence and Robotics Xi'an Jiaotong University

Synthetic aperture imaging(SAI) methods aim to see through dense occlusions and reconstruct the target scene behind occlusions. Traditional frame-based SAI methods,e.g., DeOccNet [1], take the occluded light field ima...

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A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design 24

A Sparsity-Aware Autonomous Path Planning Accelerator with A...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Zhang, Yanjun Niu, Xiaoyu Zhang, Yifan Tian, Hongzheng Yu, Bo Liu, Shaoshan Huang, Sitao Beijing Institute of Technology China University of California Irvine United States Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9798400710773

Path planning is a critical task in autonomous driving systems that is most susceptible to real-time constraints but often demands computationally intensive mathematical solvers, two contradictory goals. This conflict makes the computing of path planning a paramount challenge. At the heart of most path planners is the quadratic programming (QP) solver, which places excessive demands on the CPU in real-world autonomous driving applications. In this paper, we present an FPGA-based acceleration framework for path planning problems. Our approach leverages an operator splitting solver for quadratic programs (OSQP) and employs the preconditioned conjugate gradient (PCG) method for solving linear systems, which are customized to be more hardware-friendly than prior works. Specific memory management and parallel processing were tailored to the matrix pattern, and the incorporation of pipelining was executed to enhance throughput and execution speed. Our FPGA-based implementation achieves state-of-the-art performance against existing works, including an average 1.98× speedup compared with the state-of-the-art QP solver on Intel i7-11800H CPU, 3.90× speedup over an ARM Cortex-A57 embedded CPU, and 12.3× speedup over an NVIDIA RTX 3090 GPU. © 2024 Copyright is held by the owner/author(s).

关键词： Quadratic programming

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Seliunina, Svetlana Otelepko, Artem Memmesheimer, Raphael Behnke, Sven University of Bonn Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics Germany

ISBN: (纸本)9798331531614

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available. © 2025 IEEE.

关键词： Signal encoding

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