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IEEE Transactions on Automatic Control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

Structurally guided task decomposition in spatial navigation tasks

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arXiv 2023年

作者： He, Ruiqi Correa, Carlos G. Griffiths, Thomas L. Ho, Mark K. Max Planck Institute for Intelligent Systems Tübingen Germany Princeton Neuroscience Institute Princeton University PrincetonNJ United States Department of Psychology Princeton University PrincetonNJ United States Department of Computer Science Princeton University PrincetonNJ United States Department of Computer Science Stevens Institute of Technology HobokenNJ United States Max-Planck-Ring 4 Tübingen72076 Germany

How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment. © 2023, CC0.

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Advancing Humanoid Robots for Social Integration: Evaluating Trustworthiness Through a Social Cognitive Framework

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Advancing Humanoid Robots for Social Integration: Evaluating...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Volha Taliaronak Anna L. Lange Murat Kirtay Erhan Oztop Verena V. Hafner Department of Computer Science Adaptive Systems Group Humboldt-Universität zu Berlin Berlin Germany Science of Intelligence Research Cluster of Excellence Berlin Germany department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Symbiotic Intelligent Systems Research Center Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Ozyegin University Istanbul Turkey

Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective -that is, forming robot trust in interaction partners. Our previous robot trust model relies on assessing the trustworthiness of the interaction partners based on the computational cognitive load incurred during the interactive task [1]. However, this model does not take into account the social markers indicative of trustworthiness, such as the gestures displayed by a human partner. In this study, we make a step toward this point by extending the model by integrating a social cue processing module to achieve social human-robot interaction. This new model serves as a novel social cognitive trust framework to enable the Pepper robot to evaluate the trustworthiness of its interaction partners based on both cognitive load (i.e., the cost of perceptual processing) and social cues (i.e., their gestures). For evaluating the efficacy of the framework, the Pepper robot with the developed model is put to interact with human partners who may take the roles of a reliable, unreliable, deceptive, or random suggestion providing partner. Overall, the results indicate that the proposed framework allows the Pepper robot to differentiate the guiding strategies of the partners by detecting deceptive partners and thus select a trustworthy partner in case of a free choice to perform the next task.

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Hyper-heuristic Algorithm based on ε-Greedy for Solving the Online Seru Scheduling Problem *

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Hyper-heuristic Algorithm based on ε-Greedy for Solving the...

Chinese Control and Decision Conference, CCDC

作者： Fan Tu Yang Liu Qi Lou Fajun Liu Yang Yu Department of Intelligent Data and Systems Engineering Northeastern University Shenyang China Beijing Institute of Computer Technology and Applications Beijing China Shenyang City University Shenyang China Shanghai Science and Technology Museum Shanghai China School of Economics and Management Dalian University of Technology Dalian China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

We address an online seru scheduling problem with the uncertainty of new batch arrivals. First, the decision-making process for online seru scheduling is delineated. Subsequently, drawing on this process, we design three simple and effective scheduling rules that have different advantages. To optimize their inherent benefits, an ε-greedy based hyper-heuristic algorithm is designed to adaptively select the appropriate rule for online seru scheduling. Within this algorithmic framework, the three scheduling rules serve as low-level heuristics, while ε-greedy acts as a high-level strategy. The extensive experiments are conducted to demonstrate the performance of the proposed algorithm.

关键词： Uncertainty Heuristic algorithms Decision making Scheduling

A Cyclic Small Phase Theorem

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arXiv 2023年

作者： Chen, Chao Chen, Wei Zhao, Di Chen, Jianqi Qiu, Li KU Leuven Leuven3001 Belgium The Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China The Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between −π and π. The proposed theorem complements a cyclic version of the celebrated small gain theorem. In addition, a generalization of the proposed theorem is made via the use of angular scaling techniques for reducing conservatism. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear time-invariant system

MIMo: A Multi-Modal Infant Model for Studying Cognitive Development

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arXiv 2023年

作者： Mattern, Dominik Schumacher, Pierre López, Francisco M. Raabe, Marcel C. Ernst, Markus R. Aubret, Arthur Triesch, Jochen The Department of Computer Science and Mathematics Goethe-University Frankfurt Frankfurt am Main Germany The Max Planck Institute for Intelligent Systems Tübingen Germany The Hertie Institute for Clinical Brain Research Tübingen Germany The Frankfurt Institute for Advanced Studies Frankfurt am Main Germany

Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the human mind and may facilitate the construction of artificial minds with similar properties. Importantly, human cognitive development relies on embodied interactions with the physical and social environment, which is perceived via complementary sensory modalities. These interactions allow the developing mind to probe the causal structure of the world. This is in stark contrast to common machine learning approaches, e.g., for large language models, which are merely passively "digesting" large amounts of training data, but are not in control of their sensory inputs. However, computational modeling of the kind of self-determined embodied interactions that lead to human intelligence and consciousness is a formidable challenge. Here we present MIMo, an open-source multi-modal infant model for studying early cognitive development through computer simulations. MIMo's body is modeled after an 18-month-old child with detailed five-fingered hands. MIMo perceives its surroundings via binocular vision, a vestibular system, proprioception, and touch perception through a full-body virtual skin, while two different actuation models allow control of his body. We describe the design and interfaces of MIMo and provide examples illustrating its use. All code is available at https://***/trieschlab/MIMo. Copyright © 2023, The Authors. All rights reserved.

关键词： Binocular vision

Electrochemical impedance behavior of implantable neural interface following the change of equivalent circuit element 12

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Electrochemical impedance behavior of implantable neural int...

12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Zhang, Jing Liu, Lusheng Wang, Aiping Wang, Junkongshuai Fang, Tao Niu, Lan Bin, Jianxiong Wang, Pengchao Mu, Wei Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The simulation of two basic equivalent circuit models of the implantable neural interface is described in this paper. The simulated equivalent circuits are created based on the physicochemical mechanism of charge transfer on the surface of the implantable neural interface. Each parameter of the equivalent circuit model was varied to investigate the regular pattern of individual element affecting the performance of the implantable neural interface. In this paper, one circuit model is based on the charge transfer process, and another circuit model is based on the mixed charge transfer and diffusion process. The equivalent circuits are split into two parts. The neural interface of the implantable neural interface device is simulated in one part, and the rest of that is simulated in another part. The electrochemical impedance data is acquired firstly by simulating the equivalent circuit with Z-view software. Then, the simulated data was analyzed to investigate the change tendency of a single parameter, providing a theoretical method for developing customized implantable neural interface with specific electrochemical impedance. © 2022 IEEE.

关键词： Equivalent circuits

Exploring Humanoid Robot Face Preference Using Brain Functional Connectivity and Graph Neural Network 12

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Exploring Humanoid Robot Face Preference Using Brain Functio...

12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, Aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Huashan Hospital Fudan University Department of Rehabilitation Medicine Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Graph neural networks (GNN) have been applied in EEG signal analysis. However, it is not clear how to describe the connection relationships between electrodes, and a reasonable representation of the adjacency matrix cannot be neglected for the study of GNN based on EEG signals. In this paper, we use brain functional connectivity to analyze the face preferences of a humanoid robot and explore the feasibility of using the matrix obtained based on brain functional connectivity measurements as a GNN connectivity matrix. The results show that it is feasible and an average accuracy of 73.47% is achieved. In addition, the results of the brain functional connectivity analysis also show that there is a difference between preferences and non-preferences. © 2022 IEEE.

关键词： Anthropomorphic robots

A TB-CSP Motor Imagery EEG Recognition Method for Neural-controlled Manipulator System 12

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A TB-CSP Motor Imagery EEG Recognition Method for Neural-con...

12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Mu, Wei Wang, Pengchao Wang, Junkongshuai Fang, Tao Wang, Aiping Niu, Lan Bin, Jianxiong Liu, Lusheng Zhang, Jing Jia, Jie Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai and Robotics Ministry of Education Shanghai Engineering Research Center of Ai and Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

To develop a manipulator system based on spontaneous EEG signal control, this paper proposes a common spatial pattern method combined with the Bhattacharyya distance of time-frequency signal (TB-CSP) for EEG signal feature extraction. Firstly, for different subjects, the original EEG signal is divided by a specific time window and different frequency band;Secondly, the average Bhattacharyya distance of different frequency bands in a specific time window is calculated to select the optimal time-frequency signal and input it to CSP for feature extraction;Finally, the extracted features are input into SVM and LDA to classify and complete the control of the manipulator. The proposed method achieves 82.6% accuracy on the public dataset BCI IVa and 79.48% accuracy on the self-collected dataset. © 2022 IEEE.

关键词： Feature extraction

Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback