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Knowledge As Not Only Justified True Beliefs in Vision

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 297-299页

作者： Wenbo Zheng Fei-Yue Wang IEEE School of Computer Science and Artificial Intelligence Wuhan University of Technology Sanya Science and Education Innovation Park of Wuhan University of Technology the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.

关键词： Computational modeling Visualization Artificial intelligence Data models Visual perception Visual databases Training data

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Observer-based event-triggered formation tracking control for second-order multi-agent systems in constrained region

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science China(Information sciences) 2025年第2期68卷 151-169页

作者： Fenglan SUN Zhonghua XU Wei ZHU ■ KURTHS Key Lab of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Lab of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Department of Complexity Science Potsdam Institute for Climate Impact Research Institute of Physics Humboldt University of Berlin

In this paper, an event-triggered time-varying formation tracking control for a class of second-order nonlinear multiagent systems(MAS) operating within a constrained region is investigated. To mitigate the negative effects of external unknown disturbance, a novel disturbance observer with performance guarantees is proposed, enabling precise disturbance *** the artificial potential field(APF) method, a repulsive potential function is introduced to prevent inter-agent collisions as well as collisions with environmental obstacles. To reduce continuous communication and frequent system updates, a sliding mode technique is incorporated into the formation tracking controller, utilizing an event-triggered mechanism. The controller is also applicable to the formation control of MAS in switching-constrained regions. The achievement of the specified timevarying geometric formation is rigorously demonstrated through the Lyapunov framework. Numerical simulations are presented to validate the effectiveness of the theoretical results.

关键词： formation tracking control region constraints repulsive potential function event-triggered mechanism

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Learn-Gen-Plan: Bridging the Gap between Vision Language Models and Real-world Long-horizon Dexterous Manipulations

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IEEE Transactions on automation science and Engineering 2025年 22卷 15638-15649页

作者： Hao, Peng Cui, Shaowei Wei, Junhang Lu, Tao Cai, Yinghao Wang, Shuo Samsung R&D Institute China-Beijing Beijing100028 China Institute of Automation Beijing100190 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Beijing100190 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China

Long-horizon dexterous tasks have been a long-standing problem in robotic manipulation. Previous studies have developed task and motion planning, imitation learning, and reinforcement learning methods for long-horizon manipulations. However, these methods are hard to achieve efficient planning for new tasks. Empowered with the Vision Language Model (VLM), recent studies significantly improve the generalization of robot systems. However, these works are only verified in simple pick-and-place tasks due to limited skills. To this end, we propose the Learn-Gen-Plan (LGP), which combines the VLM and learning-based primitives to endow robots with the ability to efficiently plan and complete various long-horizon dexterous tasks. LGP contains two key phases: skill generation and task planning. In skill generation, the Skill Generator is proposed to utilize the learned key primitives and hand-crafted trivial primitives to generate adaptive robot skills. In task planning, the Multimodal Planner generates the robot plan based on image observation, generated skills, and text prompts. We set up a series of dexterous tasks (e.g., cable routing, peg-in-hole assembly) in a real-world lighting circuit wiring scenario to evaluate LGP. The experimental results show that LGP efficiently generates robot plans with learned skills, controlling the robot to complete various multi-step cable wiring tasks. © 2004-2012 IEEE.

关键词： Reinforcement learning

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SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models 31

SKIntern: Internalizing Symbolic Knowledge for Distilling Be...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liao, Huanxuan He, Shizhu Hao, Yupu Li, Xiang Zhang, Yuanzhe Zhao, Jun Liu, Kang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China National Science Library Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

Small Language Models (SLMs) are attracting attention due to the high computational demands and privacy concerns of Large Language Models (LLMs). Some studies fine-tune SLMs using Chains of Thought (CoT) data distilled from LLMs, aiming to enhance their reasoning ability. Furthermore, Some CoT distillation methods introduce external symbolic knowledge into the generation process to improve the limited knowledge memory, reasoning ability and out-of-domain (OOD) generalization of SLMs. However, the introduction of symbolic knowledge increases computational overhead and introduces potential noise. In this paper, we introduce SKIntern, an innovative approach that empowers SLMs to internalize symbolic knowledge and few-shot examples gradually through a progressive fine-tuning process, guided by a predefined linear decay schedule under curriculum learning. By efficiently internalizing knowledge, SKIntern reduces computational overhead and speeds up the reasoning process by focusing solely on the question during inference. It outperforms state-of-the-art baselines by over 5%, while reducing inference costs (measured in FLOPs) by up to 4× across a wide range of SLMs in both in-domain (ID) and out-of-domain (OOD) tasks. Our code will be available at https://***/Xnhyacinth/SKIntern. © 2025 Association for Computational Linguistics.

关键词： Cost reduction

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Robust Practical Stabilization for complex Dynamical Networks With DoS Attacks and Actuator Saturation

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3108-3118页

作者： Shi, Xueya Peng, Zhinan Yu, Junzhi Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example. © 2013 IEEE.

关键词： Lyapunov functions

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Design and Performance Analysis of a Series-Parallel Self-Aligning Index Finger Exoskeleton

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IEEE Robotics and automation Letters 2025年第6期10卷 6456-6463页

作者： Sun, Ning Wang, Zelong Cheng, Long Li, Houcheng Niu, Ben Sun, Xijin Li, Ke Shandong Normal University School of Information Science and Engineering Jinan China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Shandong University Institute of Intelligent Medical Engineering School of Control Science and Engineering Jinan China

Hand exoskeletons have become increasingly crucial for the rehabilitation of hand function, as relevant studies have shown that using the exoskeletons to assist in rehabilitation training can improve hand motor function. However, developing a human-robot kinematic compatibility exoskeleton while providing sufficient assistance torque is still a challenge. This paper develops a series-parallel self-aligning index finger exoskeleton to assist the index finger in flexion/extension and adduction/abduction training. A series-parallel mechanism driven by two motors simultaneously was proposed for the metacarpophalangeal (MCP) joint, which can ensure human-robot joint axes self-alignment and increase the joint assistance torque. A kinematic compatibility analysis method was proposed, and the kinematic compatibility of the series-parallel mechanism was analyzed. The degrees of freedom and forward kinematics of the proposed mechanism were analyzed, and its workspace was calculated, which results show that the motion range of the MCP joint can meet the grasping training requirements. Preliminary experiments were carried out, and the results indicate that the motion trajectory coefficients of the marker points at the MCP, PIP, and DIP joints averaged for all subjects with and without the proposed exoskeleton are 0.993, 0.956, and 0.939, respectively, which can verify the kinematic transparency of the proposed mechanism. The assistance torque generated by the proposed mechanism on the MCP joint can meet the joint assistance torque requirements. In a word, the proposed exoskeleton can achieve flexion/extension and adduction/abduction training, human-robot joint axes self-alignment, and provide sufficient assistance torque. © 2016 IEEE.

关键词： Exoskeleton (Robotics)

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement

arXiv

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arXiv 2025年

作者： Yuan, Xiaowei Yang, Zhao Huang, Ziyang Wang, Yequan Fan, Siqi Ju, Yiming Zhao, Jun Liu, Kang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China Meituan China University of Electronic Science and Technology of China China

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect contextual knowledge. While existing approaches focus on enhancing the decoding strategies, they ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states. As a result, LLMs remain limited in their ability to fully leverage contextual knowledge. In this paper, we propose Context-aware Layer Enhancement (CaLE), a novel intervention method that enhances the utilization of contextual knowledge within LLMs’ internal representations. By employing V-usable information analysis, CaLE strategically amplifies the growth of contextual information at an optimal layer, thereby enriching representations in the final layer. Our experiments demonstrate that CaLE effectively improves context-faithful generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge. Copyright © 2025, The Authors. All rights reserved.

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Cooperative Hierarchical Coverage Control of Multi-Agent systems with Non-holonomic Constraints in Poriferous Environments

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IEEE Transactions on Control of Network systems 2025年

作者： Zou, Jiayu Zhang, Hai-Tao Zhai, Chao Xing, Ning Ma, Yong Liu, Xingjian Huazhong University of Science and Technology School of Artificial Intelligence and Automation The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems The State Key Laboratory of Digital Manufacturing Equipment and Technology Wuhan430074 China China University of Geosciences School of Automation China The MOE Engineering Research Center of Intelligent Technology for Geo-Exploration Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Academician Workstation of China COSCO Shipping Corporation Ltd. Shanghai200120 China Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572000 China Wuhan University of Technology Chongqing Research Institute Chongqing401120 China

It has long posed a challenging task to optimally deploy multi-agent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this paper proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering non-holonomic constraints, into designated niche positions. Additionally, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the non-convex characteristics inherent in the loss function induced by porous environments. This non-convexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach. © 2014 IEEE.

关键词： Nonlinear control systems

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Exploring Social Annotations with the Application to Web Page Recommendation

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Journal of Computer science & Technology 2009年第6期24卷 1028-1034页

作者：李慧倩夏粉曾大军王飞跃毛文吉 Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Department of Management Information Systems University of Arizona

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations axe posted by users under no central control, there exist problems such as spare and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

关键词： graphic model EM （expectation-maximization） social annotation tag recommendation

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