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How can China lead global AI governance: Sustainability over competition

Bulletin of Chinese Academy of Sciences 2025年第4期40卷 706-714页

作者： Pan, Xuanming Chen, Zhenzhen Liu, Shaoshan The Guangzhou Institute of the Greater Bay Area Guangzhou 510710 China School of Law Guangdong University of Finance and Economics Guangzhou 510032 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China

In the context of great power competition and escalating geopolitical conflicts, the academic community generally holds a pessimistic view of AI international cooperation, believing that the space is shrinking. This perception mainly stems from conflicts in the cross-border supervision of AI data and algorithms among technologically advanced countries, while overlooking the strong willingness and potential for cooperation in energy conservation and emission reduction through AI technologies shown by Global South countries. Looking into the future of global governance, China, starting from leading the transformation of AI technologies, shall take lead in supporting these countries to transform the sustainable development consensus reached at the Paris AI Action Summit into concrete action plans. © 2025 Science Press. All rights reserved.

关键词： artificial intelligence competition cooperation global governance sustainable development

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FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction

arXiv

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arXiv 2025年

作者： Rotondi, Dennis Scaparro, Fabio Blum, Hermann Arras, Kai O. Socially Intelligent Robotics Lab Institute for Artificial Intelligence University of Stuttgart Germany Robot Perception and Learning Lab LAMARR Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contrast, our goal is to develop a representation that enables robots to directly interact with their environment by identifying both the location of functional interactive elements and how these can be used. To achieve this, we focus on detecting and storing objects at a finer resolution, focusing on affordance-relevant parts. The primary challenge lies in the scarcity of data that extends beyond instance-level detection and the inherent difficulty of capturing detailed object features using robotic sensors. We leverage currently available 3D resources to generate 2D data and train a detector, which is then used to augment the standard 3D scene graph generation pipeline. Through our experiments, we demonstrate that our approach achieves functional element segmentation comparable to state-of-the-art 3D models and that our augmentation enables task-driven affordance grounding with higher accuracy than the current solutions. © 2025, CC BY-NC-ND.

关键词： Semantics

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Object-Centric Image to Video Generation with Language Guidance

arXiv

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arXiv 2025年

作者： Villar-Corrales, Angel Plepi, Gjergj Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Accurate and flexible world models are crucial for autonomous systems to understand their environment and predict future events. Object-centric models, with structured latent spaces, have shown promise in modeling object dynamics and interactions, but often face challenges in scaling to complex datasets and incorporating external guidance, limiting their applicability in robotics. To address these limitations, we propose TextOCVP, an object-centric model for image-to-video generation guided by textual descriptions. TextOCVP parses an observed scene into object representations, called slots, and utilizes a text-conditioned transformer predictor to forecast future object states and video frames. Our approach jointly models object dynamics and interactions while incorporating textual guidance, thus leading to accurate and controllable predictions. Our method’s structured latent space offers enhanced control over the prediction process, outperforming several image-to-video generative baselines. Additionally, we demonstrate that structured object-centric representations provide superior controllability and interpretability, facilitating the modeling of object dynamics and enabling more precise and understandable predictions. Videos and code are available at https://***/TextOCVP/. © 2025, CC BY.

关键词： Digital elevation model

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Spectral Aggregation Cross-Square Transformer for Hyperspectral Image Denoising 27th

Spectral Aggregation Cross-Square Transformer for Hyperspect...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Liu, Yang Ji, Yantao Xiao, Jiahua Guo, Yu Jiang, Peilin Yang, Haiwei Wang, Fei School of Software Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

ISBN: (纸本)9783031783531

Hyperspectral image(HSI) denoising addresses noise impact during image acquisition. Transformers have gained notable prominence in the field of denoising, but their quadratic self-attention complexity poses computational challenges, hindering global information processing. Classical window-based self-attention limits non-local information flow, hampering large-scale object capture in HSI. Furthermore, spectral variations among neighboring bands introduce redundancy, which burdens the model and diminishes token variability, resulting in over-smoothing in the attention map. To address these issues, we propose a novel method, named Spectral Aggregation Cross-Square Transformer(SACT). We introduce a cross-square self-attention mechanism to enhance information exchange between windows, capturing long-range dependencies within spatial intra-spectrum from multiple perspectives. Spectrally, it extends the attention region horizontally, surrounding, and vertically, exploring omnidirectional spatial correlations among different receptive windows. Additionally, a spatial-spectral aggregation self-attention module is designed to capture global contextual dependencies across spatial and spectral dimensions, reducing spectral redundancy computation. Our method has evaluated synthetic and real hyperspectral datasets and shows SACT’s effectiveness in enhancing both quantitative and qualitative HSI denoising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image denoising

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Your Learned Constraint is Secretly a Backward Reachable Tube

arXiv

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arXiv 2025年

作者： Qadri, Mohamad Swamy, Gokul Francis, Jonathan Kaess, Michael Bajcsy, Andrea Carnegie Mellon University Robotics Institute United States Bosch Center for Artificial Intelligence United States

Inverse Constraint Learning (ICL) is the problem of inferring constraints from safe (i.e., constraint-satisfying) demonstrations. The hope is that these inferred constraints can then be used downstream to search for safe policies for new tasks and, potentially, under different dynamics. Our paper explores the question of what mathematical entity ICL recovers. Somewhat surprisingly, we show that both in theory and in practice, ICL recovers the set of states where failure is inevitable, rather than the set of states where failure has already happened. In the language of safe control, this means we recover a backwards reachable tube (BRT) rather than a failure set. In contrast to the failure set, the BRT depends on the dynamics of the data collection system. We discuss the implications of the dynamics-conditionedness of the recovered constraint on both the sample-efficiency of policy search and the transferability of learned constraints. © 2025, CC BY.

关键词： Contrastive Learning

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II-NVM: Enhancing Map Accuracy and Consistency With Normal Vector-Assisted Mapping

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IEEE robotics and Automation Letters 2025年第6期10卷 5465-5472页

作者： Zhao, Chengwei Li, Yixuan Jian, Yina Xu, Jie Wang, Linji Ma, Yongxin Jin, Xinglai Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hangzhou Qisheng Intelligent Techology Company Ltd Hangzhou311217 China Columbia University in the City of New York Department of Computer Science New YorkNY10027 United States Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a single plane, significantly hindering map accuracy and consistency. To addressing this issue this letter introduces a SLAM approach that ensures accurate mapping using normal vector consistency. We enhance the voxel map structure to store both point cloud data and normal vector information, enabling the system to evaluate consistency during nearest neighbor searches and map updates. This process distinguishes between the front and back sides of surfaces, preventing incorrect point-to-plane constraints. Moreover, we implement an adaptive radius KD-tree search method that dynamically adjusts the search radius based on the local density of the point cloud, thereby enhancing the accuracy of normal vector calculations. To further improve real-time performance and storage efficiency, we incorporate a Least Recently Used (LRU) cache strategy, which facilitates efficient incremental updates of the voxel map. The code is released as open-source and validated in both simulated environments and real indoor scenarios. Experimental results demonstrate that this approach effectively resolves the "double-sided mapping issue" and significantly improves mapping precision. Additionally, we have developed and open-sourced the first simulation and real-world dataset specifically tailored for the "double-sided mapping issue". © 2016 IEEE.

关键词： Mapping

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Digital Twin-Based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 14892-14905页

作者： Fu, Bo Bi, Mingjie Umeda, Shota Nakano, Takahiro Nonaka, Youichi Zhou, Quan Matsui, Takaharu Tilbury, Dawn M. Barton, Kira Department of Robotics University of Michigan Ann ArborMI48109 United States Amazon Robotics North ReadingMA01864 United States Beijing Institute for General Artificial Intelligence Beijing100080 China Hitachi Ltd. Tokyo100-8280 Japan Hitachi America Ltd. Farmington HillsMI48355 United States Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States

The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types. Note to Practitioners - This research is motivated by the need for an advanced framework that automates the reconfiguration of manufacturing lines in response to increasing disturbances within dynamic production environments. Existing approaches lack a holistic framework that automates disturbance monitoring, data management, line structure optimization, and pre-imple

关键词： Recovery

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Group Decision-Making in Robot Teleoperation: Two Heads are Better Than One 20

Group Decision-Making in Robot Teleoperation: Two Heads are ...

引用

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Nguyen, Duc-An Bhattacharyya, Raunak Colombatto, Clara Fleming, Steve Posner, Ingmar Hawes, Nick Oxford Robotics Institute University of Oxford Oxford United Kingdom Yardi School of Artificial Intelligence Indian Institute of Technology Delhi New Delhi India University of Waterloo Department of Psychology ON Canada University College London Department of Experimental Psychology London United Kingdom

ISBN: (纸本)9798350378931

Operators working with robots in safety-critical domains have to make decisions under uncertainty, which remains a challenging problem for a single human operator. An open question is whether two human operators can make better decisions jointly, as compared to a single operator alone. While prior work has shown that two heads are better than one, such studies have been mostly limited to static and passive tasks. We investigate joint decision-making in a dynamic task involving humans teleoperating robots. We conduct a human-subject experiment with N = 100 participants where each participant performed a navigation task with two mobiles robots in simulation. We find that joint decision-making through confidence sharing improves dyad performance beyond the better-performing individual (p © 2025 IEEE.

关键词： Remote control

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Stochastic-Aware Mamba Diffusion for Pedestrian Trajectory Prediction

Stochastic-Aware Mamba Diffusion for Pedestrian Trajectory P...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ziyang Ren Ping Wei Haowen Tang Huan Li Jin Yang Jialu Qin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pedestrian trajectory prediction plays a crucial role in understanding human behavior and intentions. Due to the inherent randomness in human movement, current research constructs trajectories in stochastic space and uses diffusion models to reverse the denoising process. The commonly used denoising model, Transformer, is affected by uneven noise sampling, leading to confusion between temporal consistency and randomness across multiple time points. To address this issue, we propose a novel framework named Stochastic-Aware Mamba Diffusion (SAMD), which combines Stochastic-Aware Mamba with the Diffusion model to predict motion noise and motion states in the stochastic space. It utilizes a temporal aggregator to extract temporal consistency features. We construct a motion-selective state space model, which includes the adaptive transition between consistency motion states and stochastic states for balancing stability and diversity. The motion gating unit activates pertinent information within motion states to extract motion noise for trajectory prediction. Our approach achieves state-of-the-art results on the ETH-UCY and SDD datasets while significantly reducing the computational cost.

关键词： Pedestrians Computational modeling Noise reduction Noise Stochastic processes Predictive models Feature extraction Diffusion models Trajectory Data mining

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Research on underwater target tracking method based on IPSO-LSTM

Research on underwater target tracking method based on IPSO-...

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2024 International Conference on Mechatronic Engineering and artificial intelligence, MEAI 2024

作者： Guo, Xaingyun Liu, Kaizhou Wang, Yanhong School of Artificial Intelligence Shenyang University of Technology Liaoning Province Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Province Shenyang110016 China

ISBN: (纸本)9781510689176

When tracking underwater targets, due to the limited performance of detection equipment and complex environment interference, the target data is lost in the transmission process, which affects the accuracy of target trajectory prediction. The traditional target trajectory prediction method for establishing mathematical model has some limitations and difficulty, which can not meet the requirements of real-time and accurate target trajectory prediction in underwater field. An IPSO-LSTM trajectory prediction model for underwater targets is proposed. In this method, the motion characteristics of underwater targets are analyzed, and the learning rate of LSTM is optimized by optimizing the inertia weight of particle swarm and improving the learning factor. The real trajectory data of Autonomous Underwater Vehicle were used for experimental and comparative analysis. The results show that IPSO-LSTM combined with PSO has higher prediction accuracy and better fitting effect than LSTM and PSO-LSTM. The proposed model provides a new idea for underwater moving target tracking, which has better accuracy and stability. © 2025 SPIE.

关键词： Aircraft detection

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