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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Dynamic-Horizon Model-Based Value Estimation With Latent Imagination

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第7期35卷 8812-8825页

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin 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 Peng Cheng Lab Shenzhen 518055 Peoples R China

Existing model-based value expansion (MVE) methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, a proper horizon setting is essential to world-model-based policy learning. Meanwhile, choosing an appropriate horizon value is time-consuming, especially for visual control tasks. In this article, we investigate the idea of adaptively using the model knowledge for value expansion. We propose a novel world-model-based method called dynamic-horizon MVE (DMVE) to adjust the use of the world model with adaptive rollout horizon selection. Based on the reconstruction-based technique, the raw and reconstructed images are both used to obtain multihorizon rollouts by utilizing latent imagination. Then, a horizon reliability degree detection approach is given to select appropriate horizons and obtain more accurate value estimation by the reconstructed value expansion errors. Experimental results on the mainstream benchmark visual control tasks show that DMVE outperforms all baselines in sample efficiency and final performance. In addition, experiments on the autonomous driving lane-changing task further demonstrate the scalability of our method. The codes of DMVE are available at https://***/JunjieWang95/dmve.

关键词： Task analysis Adaptation models Image reconstruction Visualization Estimation Heuristic algorithms Computational modeling Latent world model model-based value expansion (MVE) reinforcement learning rollout horizon

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ArtVerse: A Paradigm for Parallel Human-Machine Collaborative Painting Creation in Metaverses

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2200-2208页

作者： Guo, Chao Dou, Yong Bai, Tianxiang Dai, Xingyuan Wang, Chunfa Wen, Yi Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Natl Museum China Beijing 100006 Peoples R China Natl Museum China Inst Sci & Art Beijing 100006 Peoples R China

Currently, the development of the foundation model, metaverse, nonfungible token (NFT), and other emerging technologies has brought profound effects on the whole art field, including art creation, dissemination, transaction, etc. However, there is no research focusing on the framework, methodologies, and applications of the human-machine collaborative creation in the metaverse era. Based on parallel theory, this article proposes a novel human-machine collaborative creation paradigm called ArtVerse, in which machines take on the roles of humans to perform creation exploration and evolution and build decentralized art organizations. Besides, the operational processes involving several key technologies are designed to achieve the proposed ArtVerse. Then, a prototype system of ArtVerse, our long-term efforts toward the human-machine collaborative painting, is presented. Finally, a new ecology of artistic creation in the metaverse era is demonstrated through the applications of the ArtVerse.

关键词： Artificial intelligence (AI) art artificial intelligence generated content (AIGC) parallel theory co-creation cyber-physical-social system (CPSS) decentralized/distributed autonomous operations and organizations (DAOs) foundation model human-machine collaboration metaverse robotic painting

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Toward Sustainable Intelligent Transportation systems in 2050: Fairness and Eco-Responsibility

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3537-3540页

作者： Chen, Jicheng Tao, Siyou Teng, Siyu Chen, Yuanyuan Zhang, Hui Wang, Fei-Yue Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Engn Macau 999078 Peoples R China

The Paris Agreement set global energy conservation and emission reduction standards in 2016, and the ambitious goal of carbon neutrality must be achieved by 2050. However, the conflicts between environmental protection, economic development and social fairness constrain the development of sustainable transportation systems. At the same time, the automotive industry believes that large-scale application of autonomous driving technology is expected by 2050. The development and application of autonomous driving technology can provide a new solution to improve the sustainability of the transportation system. Meanwhile, autonomous vehicles can significantly enhance the mobility of people with disabilities and seniors and reduce the transportation cost for poverty people, improving the transportation fairness of the entire society. This letter discusses the impact of autonomous driving technology on sustainable transportation systems from the perspectives of social fairness and economic responsibility, as a brief summary of the third part of Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL).

关键词： Sustainability autonomous vehicles social fairness carbon neutrality green transportation

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Autogeneration of Mission-Oriented Robot controllers Using Bayesian-Based Koopman Operator

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 903-918页

作者： Pan, Jie Li, Dongyue Wang, Jian Zhang, Pengfei Shao, Jinyan Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China 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 Nanchang Innovat Inst Nanchang 330096 Peoples R China

Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input-output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control.

关键词： Bayesian optimization (BO) control-oriented model identification Koopman operator robot control

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Adaptive Dynamic Programming for Nonlinear-Constrained H8 control

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第7期53卷 4393-4403页

作者： Yang, Xiong Xu, Mengmeng Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article considers the H-infinity control problem of nonlinear systems having unavailable dynamics and asymmetric saturating actuators. Initially, such an H-infinity control problem is converted into the zero-sum game with a nonquadratic cost function being introduced. Then, in order to solve the Hamilton-Jacobi-Isaacs equation arising in the zero-sum game, a simultaneous policy iteration (SPI) algorithm is developed under the adaptive dynamic programming framework. Meanwhile, it is proved that the convergence of the SPI algorithm in essence amounts to the convergence of the sequential PI algorithm. To implement the SPI algorithm, the critic, the actor, and the perturbation neural networks (NNs) are, respectively, constructed to estimate the cost function, the control policy, and the perturbation. The three NNs' weights are simultaneously determined by using the least-squares method together with the Monte Carlo integration technique. A remarkable characteristic of such an SPI algorithm is that arbitrary control policies and perturbations are applicable in the learning process. This makes system's information be able to be replaced by the data collected along system's trajectories in advance. More importantly, the persistence of the excitation condition is not required. Finally, simulations of two nonlinear examples are given to validate the present SPI algorithm.

关键词： Perturbation methods Games Game theory Nonlinear systems Heuristic algorithms Artificial neural networks Dynamic programming Adaptive approximate dynamic programming (ADP) asymmetric constraint neural network (NN) control simultaneous policy iteration (SPI)

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VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching System in Cyber-Physical-Social Space

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3501-3513页

作者： Shen, Tianyu Huang, Shi-Sheng Li, Deqi Lu, Zhiyuan Wang, Fei-Yue Huang, Hua Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Lecturers, as the guidance of the classroom, play a significant role in the teaching process. However, the lecturers' sense of space immersion has been ignored in current virtual teaching systems. In this article, we explore the cyber-physical-social intelligence for Edu-Metaverse in cyber-physical-social space and specially design a lecturer-centered immersive teaching system, taking the social and lecturers' factors into consideration. We call this system VirtualClassroom (V-Classroom). Specifically, we first introduce the cyber-physical-social system (CPSS) paradigm of V-Classroom so that the workflow is standardized and significantly simplified, and the systems can be constructed with off-the-shelf hardware. The key component of V-Classroom is a cyber-world representation of a physical-world classroom instrumented with sparse consumer-grade RGBD cameras for capturing the 3-D geometry and texture of the classrooms. We provide each V-Classroom lecturer with a physical device for sending 6DoF view-change messages and showing view-dependent content of the remote classroom. Following the above paradigm, we develop the V-Classroom algorithms, including V-Classroom depth algorithm (V-DA) and V-Classroom view algorithm (V-VA), to achieve the real-time rendering of remote classrooms. V-DA is dedicated to recovering accurate depth information of the classrooms while V-VA is devoted to real-time novel view synthesis. Finally, we illustrate our implemented CPSS-driven V-Classroom prototype, based on real-world classroom scenarios we collected, and discuss the main challenges and future direction.

关键词： 6DoF video cyber-physical-social systems (CPSSs) educational metaverse immersive teaching

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A New Architecture and Mechanism for Decentralized Science MetaMarkets

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第9期53卷 5321-5330页

作者： Ding, Wenwen Li, Juanjuan Qin, Rui Kozma, Robert Wang, Fei-Yue Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Memphis Dept Math Memphis TN 38152 USA Obuda Univ H-1034 Budapest Hungary Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The new generation of digital intelligence technology enables knowledge creation, dissemination, and application to undergoing parallel changes. Scientific systems face an increasingly uncertain, diverse, and complex environment, making adopting multidisciplinary, interdisciplinary, and transdisciplinary approaches to research issues inevitable. Existing scientific systems follow linear value streams, leading to problems, such as inefficiency, unfairness, and knowledge monopoly. Decentralized science (DeSci) is a new scientific development paradigm based on Web3, Metaverses, and decentralized autonomous organizations and operations (DAOs) technologies, that can solve organizational and management problems in scientific systems through organizing, coordinating, and executing techniques. However, new economic theories and methods are still needed to effectively solve the problem of linear value flow in scientific systems. Metaeconomics based on the parallel intelligence theory, also known as decentralized economics (DeEco), provides a new approach and idea for redesigning the economic system of scientific markets. Thus, this article proposes a research framework and core mechanisms of DeSci MetaMarkets based on parallel economic theory to provide effective and practical methodologies for scientific system governance.

关键词： Economics Production Knowledge engineering Organizations complex systems Personnel Monopoly Decentralized autonomous organizations and operations (DAOs) decentralized autonomous operations and organizations decentralized science (DeSci) decentralized economics (DeEco) decentralized management (DeMana) DeSci metaeconomics MetaMarkets metaverses parallel intelligence Web3

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EEG-Based Focus of Attention Tracking and Regulation During Dual-Task Training for Neural Rehabilitation of Stroke Patients

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2023年第3期70卷 920-930页

作者： Wang, Jiaxing Wang, Weiqun Hou, Zeng-Guang Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China

Dual-task training under variable-priority instructions (DT-VP), during which subjects are required to vary their focus of attention (FOA) between two concurrent tasks, has shown a more significant improvement in neural rehabilitation than that under fixed-priority instructions. Failed FOA switching not only diminishes the recovery benefits, but also causes anxieties, which is detrimental to rehabilitation. Developing a strategy for tracking and regulating patients' FOA to achieve a better performance in task priority-following during DT-VP is thus imperative. In this study, fifteen stroke patients participated in DT-VP that comprised two tasks: a mathematical problem-solving task and a cycling task, during which their electroencephalograms were recorded simultaneously. The significantly differentiated power spectra of four brain regions extracted from single-task training were fed into a support vector machine to build a FOA tracking algorithm for patients' attention assessment during the DT-VP. Moreover, dual-task difficulty adaptation method was designed to regulate patients' FOA when their FOA and the high-priority task were not coincident. The comparison experimental results showed that the proposed method significantly improved patients' FOA distributed on the high-priority task (analysis of variance, p < 0.05). Meanwhile, the absolute power spectral densities of the motor cortex and the frontal region could also be improved during DT-VP under high motor and cognitive task priority instructions, respectively. These phenomena demonstrated the feasibility of the proposed method in helping stroke patients better implement FOA switching and maintenance.

关键词： Task analysis Training Stroke (medical condition) Electroencephalography Regulation Mathematical models Switches Neural rehabilitation brain-computer interface attention regulation difficulty adaptation task priority-following

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learning anomalous human actions using frames of interest and decoderless deep embedded clustering

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第10期14卷 3575-3589页

作者： Javed, Muhammad Hafeez Yu, Zeng Li, Tianrui Anwar, Noreen Rajeh, Taha M. M. Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Chinese Acad Sci Control Complex Syst Inst Automation State Key Lab Management Beijing Peoples R China

Inconsistent data and unclear labels make it difficult to learn anomalous behavior from video. Therefore, methods based on deep clustering are now trending in this area. A deep clustering strategy usually relies on encoding and reconstruction to facilitate information discovery. However, it seems pointless to reconstruct the input after the model's learning process is already concluded. On the other hand, multiple input types carry various features which may help identify the problem more accurately. Hence to mitigate the requirement of utilizing assorted features with clustering, we propose Skeletal Based Autoencoder (SKELBA), which allows us to process the different types of inputs parallelly. The model consists of a spatial graph convolution operator, which helps us convolve the skeletal data more precisely. A decoder-less deep clustering architecture is introduced to enhance the stability of clustering. The relation between reconstruction error and minimizing the lower bound of mutual information (MI) helps us look into decoder-free systems. The joint venture of local-global feature collection and decoder-free encoders techniques shows improved results. Extensive experiments performed on the various benchmark datasets highlight the proposed model's superiority among recently proposed approaches in the same field.

关键词： Deep learning

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