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Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 40-53页

作者： Qinglai Wei Shanshan Jiao Qi Dong Fei-Yue Wang IEEE the Institute of Systems Engineering Macau University of Science and Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences China Academy of Electronics and Information Technology the State Key Laboratory of Multimodal Artificial Intelligence Systems the School of Artificial Intelligence University of Chinese Academy of Sciences

This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.

关键词： Adaptive dynamic programming (ADP) critic neural network (NN) event-triggered control optimal consensus control robust control

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Learning future representation with synthetic observations for sample-efficient reinforcement learning

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Science China(Information Sciences) 2025年第5期68卷 20-37页

作者： Xin LIU Yaran CHEN Haoran LI Dongbin ZHAO State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Image-based reinforcement learning(RL) has proven its effectiveness for continuous visual control of embodied agents, where upstream representation learning largely determines the effectiveness of policy learning. Employing selfsupervised auxiliary tasks allows the agent to enhance visual representation in a targeted manner, thereby improving the policy performance and the RL sample efficiency. Prior advanced self-supervised RL methods all try to design better auxiliary objectives to extract more information from agent experience, while ignoring the training data constraints caused by experience limitations in RL training. In this article, we first try to break through this auxiliary training data constraint,proposing a novel RL auxiliary task named learning future representation with synthetic observations(LFS), which improves the self-supervised RL by enriching auxiliary training data. Firstly, a novel training-free method, named frame mask, is proposed to synthesize novel observations that may contain future information. Next, the latent nearest-neighbor clip(LNC)is correspondingly proposed to alleviate the impact of unqualified noise in synthetic observations. The remaining synthetic observations and real observations then together serve as the auxiliary training data to achieve a clustering-based temporal association task for advanced representation learning. LFS allows the agent to access and learn observations that are not present in the current experience but will appear in future training, thus enabling comprehensive visual understanding and an efficient RL process. In addition, LFS does not rely on rewards or actions, which means it has a wider scope of application(e.g., learning from video) than recent advanced RL auxiliary tasks. We conduct extensive experiments on challenging continuous visual control of complex embodied agents, including robot locomotion and manipulation. The results demonstrate that our LFS exhibits state-of-the-art sample efficiency

关键词： deep reinforcement learning (DRL) RL for embodied agents RL for continuous control image-based RL self-supervised learning RL visual pre-training

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Residential Energy Scheduling With Solar Energy Based on Dyna Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 403-413页

作者： Kang Xiong Qinglai Wei Hongyang Li the Institute of Systems Engineering Macau University of Science and Technology IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences the School of Artificial Intelligence University of Chinese Academy of Sciences

Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.

关键词： Adaptive dynamic programming (ADP) dynamic residential scenarios optimal residential energy management smart grid

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Unveiling the Hidden Interactions Among Features:A Heterogeneous Graph Approach for Personality Prediction

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Machine intelligence Research 2025年第1期22卷 91-100页

作者： Yuxuan Song Yilin Wu Qiudan Li Liping Chen Daniel Zeng The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China The School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China

Identifying personalities accurately helps merchants and management departments understand user needs in detail and improve the quality of service and decision-making *** research on text-based personality prediction mainly uses deep neural networks or pretrained language models to mine deep semantics,ignoring the dynamic interactions among personality *** paper presents a novel personality prediction method that simultaneously taps into the capability of graph neural networks to model the deep interactions among features and that of pretrained language models to learn latent semantics with a hierarchical aggregation ***,the proposed model leverages self-attention to capture the interaction relationships among POS tags,entities,personality tags,etc.,and considers the labels’cooccurrence *** efficacy of the proposed model is evaluated on the myPersonality and PANDORA *** research contributes to the personality prediction literature from the perspective of a multigranular personality feature learning perspective and provides business value for consuming predictive analytics.

关键词： Personality prediction graph representation learning pretrained models graph convolutional networks attention mechanism

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Hydrodynamic Characteristic Analysis of a Biomimetic Underwater Vehicle-Manipulator System

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Journal of Bionic Engineering 2025年第2期22卷 654-669页

作者： Hongfei Chu Xiaolong Hui Xuejian Bai Min Tan Yu Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100000China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100000China School of Electrical Engineering Liaoning University of TechnologyJinzhou121000China

The propulsion mechanisms of biomimetic underwater vehicles using bionic undulatory fins have been extensively studied for their potential to enhance efficiency and maneuverability in underwater ***,the hydrodynamic interactions between the vehicle body,robotic manipulator,and fluctuating motion remain less explored,particularly in turbulent *** this work,a Biomimetic Underwater Vehicle-Manipulator System(BUVMS)propelled using bionic undulatory fins is *** propulsion mechanism and hydrodynamic performance of fluctuating motion are analyzed by numerical *** drag coefficients of the BUVMS at different Reynolds numbers are calculated,and the investigation of vortex generation during the motion of the BUVMS reveals that vortex binding and shedding are the key factors for propulsion *** moving modes of the BUVMS are developed in conjunction with the pro-pulsion *** hydrodynamic loads during the motion of the underwater robotic arm in a turbulent environment are analyzed.A simple motion strategy is proposed to reduce the effect of water drag on the manipulation of the robotic arm and on the overall stability of the *** results of the hydrodynamic analysis offer systematic guidance for controlling underwater operations of the BUVMS.

关键词： Biomimetic underwater vehicle-Manipulator system(BUVMS) Computational fluid dynamics(CFD) Hydrodynamics Bionic undulatory fins Propulsion mechanism

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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Improving Multi-task GNNs for Molecular Property Prediction via Missing Label Imputation

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Machine intelligence Research 2025年第1期22卷 131-144页

作者： Fenyu Hu Dingshuo Chen Qiang Liu Shu Wu University of Chinese Academy of Sciences Beijing100190China Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence SystemsInstitute of AutomationChinese Academy of SciencesBeijing100190China

The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there is a great need to develop the multi-task learning ability of ***,limited by expensive and time-consuming human annotations,collecting complete labels for each task is *** a result,most existing benchmarks involve many missing labels in training data,and the performance of GNNs is impaired due to the lack of sufficient supervision *** overcome this obstacle,we propose to improve multi-task molecular property prediction by missing label ***,a bipartite graph is first introduced to model the molecule-task co-occurrence ***,the imputation of missing labels is transformed into predicting missing edges on this bipartite *** predict the missing edges,a graph neural network is devised,which can learn the complex molecule-task co-occurrence *** that,we select reliable pseudo labels according to the uncertainty of the prediction *** with enough and reliable supervision information,our approach achieves state-of-the-art performance on a variety of real-world datasets.

关键词： Graph classification imbalance learning prediction bias mixture of experts multiview representations

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Task-Parameterized Dynamic Movement Primitives With Reinforcement Learning for Improved Motion Planning

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IEEE Robotics and automation Letters 2025年第6期10卷 5457-5464页

作者： Huang, Kaiqi Ji, Xiaochun Su, Jianhua Qu, Xiaoyi Jiangxi University of Science and Technology Ganzhou341000 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China

Online trajectory planning in unstructured environments poses significant challenges for mobile robots, particularly when navigating complex obstacles. Traditional learning-from-demonstration (LfD) methods depend on offline datasets, limiting their ability to adapt to varying obstacle shapes and dynamic conditions. To address these limitations, we propose a novel motion planning framework that combines global trajectory generation with local adaptability. Dynamic Movement Primitives (DMPs) are employed to generate global trajectories based on demonstrations, while Task-Parameterized Potential Fields (TPPFs) enhance local adaptability. The Policy Improvement through Path Integrals (PI2) algorithm is utilized to optimize model parameters. The TPPF framework consists of two key components: (a) an obstacle avoidance field, which accounts for the robot's size, obstacle dimensions, and relative distances, allowing effective volumetric avoidance without extensive modeling;and (b) an attractive field, which directs the robot toward task-specific goals while steering it away from undesirable paths. By leveraging the PI2 algorithm, model parameters are optimized to produce trajectories that preserve the characteristics of demonstrated motions, while improving obstacle avoidance and task-oriented navigation. Experiments conducted in both simulations and dynamic real-world scenarios validate the proposed framework's effectiveness, demonstrating smoother trajectories and enhanced obstacle avoidance compared to baseline approaches. © 2016 IEEE.

关键词： Trajectories

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SweetieChat: A Strategy-Enhanced Role-playing Framework for Diverse Scenarios Handling Emotional Support Agent 31

SweetieChat: A Strategy-Enhanced Role-playing Framework for ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ye, Jing Xiang, Lu Zhang, Yaping Zong, Chengqing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have demonstrated promising potential in providing empathetic support during interactions. However, their responses often become verbose or overly formulaic, failing to adequately address the diverse emotional support needs of real-world scenarios. To tackle this challenge, we propose an innovative strategy-enhanced role-playing framework, designed to simulate authentic emotional support conversations. Specifically, our approach unfolds in two steps: (1) Strategy-Enhanced Role-Playing Interactions, which involve three pivotal roles—Seeker, Strategy Counselor, and Supporter—engaging in diverse scenarios to emulate real-world interactions and promote a broader range of dialogues;and (2) Emotional Support Agent Training, achieved through fine-tuning LLMs using our specially constructed dataset. Within this framework, we develop the ServeForEmo dataset, comprising an extensive collection of 3.7K+ multi-turn dialogues and 62.8K+ utterances. We further present SweetieChat, an emotional support agent capable of handling diverse open-domain scenarios. Extensive experiments and human evaluations confirm the framework's effectiveness in enhancing emotional support, highlighting its unique ability to provide more nuanced and tailored assistance. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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FAWL: Weakly-Supervised Video Corpus Moment Retrieval with Frame-Wise Auxiliary Alignment and Weighted Contrastive Learning

FAWL: Weakly-Supervised Video Corpus Moment Retrieval with F...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Pan, Yi Zhang, Yujia Zhao, Xiaoguang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9798350368741

Video Corpus Moment Retrieval (VCMR) is a challenging task that aims to localize query-specified moments from a collection of untrimmed videos. The recent state-of-the-art method, JSG, tries to tackle this task using only video-level annotations in a weakly-supervised setting. However, the late fusion strategy of JSG suffers from insufficient alignment, and the proposal-level video representations aggregated with Gaussian Distributions result in semantic inconsistency across frames. To address these issues, we propose a novel weakly-supervised VCMR method, FAWL, which incorporates frame-wise auxiliary alignment and weighted contrastive learning. Two frame-wise auxiliary alignment tasks, namely Query-guided Saliency Alignment (QSA) and Event-aware Boundary Alignment (EBA), are designed first. During training, QSA projects video and text representations through a shared layer, and computes frame-level saliency losses for sufficient multimodal alignment. EBA guides frames to predict distances to proposal boundaries making each frame aware of the corresponding events and thus ensuring semantic consistency. To help the model distinguish proposals with similar visual semantics, we further propose the Weighted Contrastive Learning (WCL) which integrates inter-video similarities into the conventional InfoNCE loss. FAWL enjoys both improved alignment and inference efficiency, achieving new state-of-the-art performance on two challenging datasets. Code is available at https://***/BUAAPY/FAWL. © 2025 IEEE.

关键词： Contrastive Learning multimodal Alignment Video Corpus Moment Retrieval

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