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7th IEEE International Conference on Advanced Robotics and Mechatronics

作者： Chen, Jiahao Huang, Xiao Wang, Xiaona Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Inst Technol Sch Mechatron Engn Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

The musculoskeletal robot has become a promising research direction. However, the control problem of the musculoskeletal robot still limits its application. Especially in the real-world application, only some incomplete and imprecise feedback states could be obtained due to the limitation of sensors. Therefore, this paper proposes a recurrent neural network (RNN) based partially observed feedback control method of musculoskeletal robots. The RNN has the ability of working memory and can realize muscle control through implicitly inferring sufficient states from partially observed states. It can also generate muscle excitations with synergies to guarantee great generalization. The effectiveness of the proposed method is verified on a simulated musculoskeletal system. Compared with other deep reinforcement learning method, the proposed method achieves similar performance under sufficient feedback states and better performance under partially observed feedback states.

关键词： Deep learning Recurrent neural networks Mechatronics Neuromuscular Reinforcement learning Robot sensing systems Hardware

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Proactivity of fish and leadership of self-propelled robotic fish during interaction

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BIOINSPIRATION & BIOMIMETICS 2023年第3期18卷 036011-036011页

作者： Zhou, Ziye Liu, Jincun Pan, Jie Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Fish interacting with biomimetic robotic fish is beneficial for animal behavior research, particularly in the study of collective behavior. Compared with passive-dragging robotic fish, self-propelled robotic fish floats in water, and its movement matches the flow field formed by the caudal fin oscillation, leading to more realistic interaction with animals. In this paper, we propose a self-propelled koi-mimicking robotic fish entity, develop a system for robotic fish and koi fish interaction, and conduct extensive experiments on quantity variation and parameter variation. The results showed that fish exhibited significantly lower proactivity when alone, and the most proactive case is one robotic fish interacting with two real fish. The experiments on parameter variation indicated that fish may respond more proactivity to robotic fish that swim with high frequency and low amplitude, but may also move together with high-frequency and high-amplitude swimming robotic fish. These findings could provide insights into fish collective behavior, guide the design of further fish-robot interaction experiments, and suggest directions for future improvements in goal-oriented robotic fish platforms.

关键词： biomimetics animal interaction robotic fish self-propelled

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SWDPM: A Social Welfare-Optimized Data Pricing Mechanism

arXiv

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arXiv 2023年

作者： Yu, Yi Yao, Shengyue Wang, Fei-Yue Li, Juanjuan Lin, Yilun Urban Computing Lab Shanghai AI Laboratory Shanghai China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Macau Institute of Systems Engineering Macau University of Science and Technology China

Data trading has been hindered by privacy concerns associated with user-owned data and the infinite reproducibility of data, making it challenging for data owners to retain exclusive rights over their data once it has been disclosed. Traditional data pricing models relied on uniform pricing or subscription-based models. However, with the development of Privacy-Preserving Computing techniques, the market can now protect the privacy and complete transactions using progressively disclosed information, which creates a technical foundation for generating greater social welfare through data usage. In this study, we propose a novel approach to modeling multi-round data trading with progressively disclosed information using a matchmaking-based Markov Decision Process (MDP) and introduce a Social Welfare-optimized Data Pricing Mechanism (SWDPM) to find optimal pricing strategies. To the best of our knowledge, this is the first study to model multi-round data trading with progressively disclosed information. Numerical experiments demonstrate that the SWDPM can increase social welfare 3 times by up to 54% in trading feasibility, 43% in trading efficiency, and 25% in trading fairness by encouraging better matching of demand and price negotiation among traders. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Fusing Knowledge and Experience with Graph Convolutional Network for Cross-task Learning in Visual Cognitive Development 5th

Fusing Knowledge and Experience with Graph Convolutional Ne...

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5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Meituan-Dianping Group Beijing100190 China

ISBN: (纸本)9789811623356

Visual cognitive ability is important for intelligent robots in unstructured and dynamic environments. The high reliance on large amounts of data prevents prior methods to handle this task. Therefore, we propose a model called knowledge-experience fusion graph (KEFG) network for novel inference. It exploits information from both knowledge and experience. With the employment of graph convolutional network (GCN), KEFG generates the predictive classifiers of the novel classes with few labeled samples. Experiments show that KEFG can decrease the training time by the fusion of the source information and also increase the classification accuracy in cross-task learning. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Learning Hierarchical Graph Convolutional Neural Network for Object Navigation 31st

Learning Hierarchical Graph Convolutional Neural Network for...

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31st International Conference on Artificial Neural Networks (ICANN)

作者： Xu, Tao Yang, Xu Zheng, Suiwu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Huizhou Zhongke Adv Mfg Ltd Co Huizhou Peoples R China

ISBN: (纸本)9783031159312;9783031159305

The goal of object navigation is to navigate an agent to a target object using visual input. Without GPS and the map, one challenge of this task is how to locate the target object in the unseen environment, especially when the target object is not in the field of view. Previous works use relation graphs to encode the concurrence relationships among all the object categories, but these relation graphs are usually too flat for the agent to locate the target object efficiently. In this paper, a Hierarchical Graph Convolutional Neural Network (HGCNN) is proposed to encode the object relationships in a hierarchical manner. Specifically, the HGCNN consists of two graph convolution blocks and a graph pooling block, which constructs the hierarchical relation graph by learning an area-level graph from the object-level graph. Consequently, the HGCNN based framework enables the agent to locate the target object efficiently in the unseen environment. The proposed model is evaluated in the AI2-iTHOR environment, and the performance of object navigation shows a significant improvement.

关键词： Object navigation Hierarchical relation graph AI2-iTHOR

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Traffic Signal control Using Offline Reinforcement Learning

Traffic Signal Control Using Offline Reinforcement Learning

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2021 China Automation Congress, CAC 2021

作者： Dai, Xingyuan Zhao, Chen Li, Xiaoshuang Wang, Xiao Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781665426473

The problem of traffic signal control is essential but remains unsolved. Some researchers use online reinforcement learning, including the off-policy one, to derive an optimal control policy through interaction between agents and environments in simulation. However, it is difficult to deploy the policy in real transportation systems due to the gap between simulated and real traffic data. In this paper, we consider an offline reinforcement learning method to tackle the problem. First, we construct a realistic traffic environment and obtain offline data based on a classic actuated traffic signal controller. Then, we use an offline reinforcement learning algorithm, namely conservative Q-learning, to learn an efficient control policy via offline datasets. We conduct experiments on a typical road intersection and compare the conservative Q-learning policy with the actuated policy and two data-driven policies based on off-policy reinforcement learning and imitation learning. Empirical results indicate that in the offline-learning setting the conservative Q-learning policy performs significantly better than other baselines, including the actuated policy, but the other two data-driven policies perform poorly in test scenarios. © 2021 IEEE

关键词： Reinforcement learning

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Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking 17

Visual Pencil: Design of Portable Human-Computer Interaction...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Tong, Ru Wang, Tianzhu Yu, Junzhi Institute of Automation Cas State Key Lab Management and Control for Complex Systems Beijing100190 China

ISBN: (纸本)9781728164151

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers. © 2020 IEEE.

关键词： Human computer interaction

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Robotic Intra-Operative Ultrasound:Virtual Environments and Parallel systems

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1095-1106页

作者： Shuangyi Wang James Housden Tianxiang Bai Hongbin Liu Junghwan Back Davinder Singh Kawal Rhode Zeng-Guang Hou Fei-Yue Wang the State Key Laboratory of Management and Control of Complex Systems Chinese Academy of SciencesInstitute of AutomationBeijing 100190China the School of Biomedical Engineering and Imaging Sciences King’s College LondonLondon SE17EHUK the Xtronics Ltd.Gravesend DA122ADUK

Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed *** towards automatic or semi-automatic ultrasound,being able to define ultrasound views and the corresponding probe poses via intelligent approaches become *** on the concept of parallel system which incorporates the ingredients of artificial systems,computational experiments,and parallel execution,this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such *** proposed virtual system includes the use of 3 D slicer as the main workspace and graphic user interface(GUI),Matlab engine to provide robotic control algorithms and customized functions,and PLUS(Public software Library for Ultra Sound imaging research)toolkit to generate simulated ultrasound *** implementation methods were presented and the proposed features of the system were *** on this virtual system,example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE *** includes standard view definition and customized view optimization for pre-planning and navigation,as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose *** conclude,the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

关键词： Medical robots parallel intelligence parallel systems robotic-assisted surgery robotic ultrasound

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A Novel Sparse Geometric 3-D LiDAR Odometry Approach

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IEEE systems JOURNAL 2021年第1期15卷 1390-1400页

作者： Liang, Shuang Cao, Zhiqiang Guan, Peiyu Wang, Chengpeng Yu, Junzhi Wang, Shuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Dept Mech & Engn Sci Coll EngnState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Localization is a fundamental prerequisite, no matter whether a single robot or multirobot system, where light detection and ranging (LiDAR) odometry has attracted great interest with accurate depth information and robustness to illumination variations. In this article, a novel 3-D LiDAR odometry approach based on sparse geometric information is proposed. Different from geometric map-based 3-D LiDAR odometry methods with point features, we concern significant line and plane features based on eigenvalues of neighboring points. Furthermore, line-to-line and plane-to-plane associations instead of point-to-line and point-to-plane associations are adopted, and the problem of high computation complexity for scan-to-map matching module caused by point feature is solved. The proposed approach can not only guarantee the accuracy of pose estimation but also reduce computation complexity. Experiments on the public KITTI dataset and an outdoor scenario demonstrate the effectiveness of our approach in terms of accuracy and efficiency.

关键词： Laser radar Feature extraction Simultaneous localization and mapping Three-dimensional displays Computational complexity Distance measurement Lighting Line and plane features line-to-line and plane-to-plane associations sparse geometric map 3-D light detection and ranging (LiDAR) odometry

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Unravel the distinct effects of adiposity at different life stages on COVID-19 susceptibility and severity: A life-course Mendelian randomization study

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JOURNAL OF MEDICAL VIROLOGY 2024年第10期96卷 e29943页

作者： Xiao, Pei Li, Chi Wu, Jinyi Dai, Jiayuan Capital Med Univ Beijing Childrens Hosp Ctr Noncommunicable Dis Management Natl Ctr Childrens Hlth Beijing Peoples R China Shijingshan Dist Ctr Dis Control & Prevent Dept AIDS STD Control & Prevent Beijing Peoples R China Wuhan Fourth Hosp Dept Publ Hlth Wuhan Peoples R China Fudan Univ Sch Publ Hlth Shanghai Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Peking Union Med Coll Hosp Dept Rare Dis State Key Lab Complex Severe & Rare Dis Beijing Peoples R China

Childhood obesity is widely recognized as a risk factor for numerous health conditions, particularly cardiovascular disease. However, it remains unclear whether childhood adiposity directly affects the risk of COVID-19 in later life. We aimed to investigate the causal effects of early life adiposity on COVID-19 susceptibility and severity. We used genetic instruments from large-scale genome-wide association studies to examine the relationships between birth weight, childhood and adulthood adiposity indicators (including body mass index [BMI], obesity, and body size), and COVID-19 outcomes. Univariable and multivariable Mendelian randomization (MR) analyses were used to obtain the causal estimates. Univariable MR analyses found that childhood BMI and obesity were positively associated with COVID-19 risk and severity in adulthood, however, the significant associations were attenuated to null after further adjusting for adulthood adiposity indicators in multivariable MR analyses. In contrast, our analysis revealed strong evidence of a genetically predicted effect of childhood obesity on COVID-19 hospitalization (OR 1.08, 95% CI: 1.01-1.15, p = 2.12E-2), which remained robust even after adjusting for adulthood obesity and potential lifestyle confounders. Our results highlight the importance of promoting healthy weight management throughout life to reduce the risk of COVID-19.

关键词： causal effect childhood adiposity COVID-19 Mendelian randomization

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