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arXiv 2024年

作者： Quenzel, Jan Mallwitz, Linus T. Arnold, Benedikt T. Behnke, Sven Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Germany Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Fraunhofer FIT Germany Fraunhofer IAIS Germany

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Novel Dynamic Feature Fusion Stragegy for Detection of Small Underwater Marine Object 5

Novel Dynamic Feature Fusion Stragegy for Detection of Small...

引用

5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Chen, Jie Joo, Meng Zhang, Yani Gao, Wenxiao Wu, Jianguo Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Tianjin Hanhai Lanfan Marine Technology Co. Ltd China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Underwater marine object detection has been shown to be significant in the community of marine science and engineering, and has been extensively employed in many practical applications, including commercial fisheries, marine exploration, etc. A typical small object detection trap frequently exists due to the fact that the majority of objects of interest in the underwater environment are usually small. Detection of small marine object is highly desired. This paper presents a novel dynamic feature fusion algorithm based on spatial-Aware and scale-Aware attention that adaptively fuses different scale feature maps for the detection of small underwater marine objects. Leveraging the proposed dynamic feature fusion algorithm, an improved YOLOX-DFF model is proposed to detect marine organisms in natural seabed environments. To alleviate conflicts between classification and regression tasks in the detection head, the decoupled head of YOLOX-DFF is also augmented with a task-Aware attention module. The proposed model surpasses state-of-The-Art methods by a large margin of +0.7mAP, and +2.3AP{{S}} on the DUO dataset. The effectiveness and efficiency of each module are further demonstrated by the ablation studies on PASCAL VOC 0712 datasets, achieving excellent performance with +1.3mAP, and +3.0AP{S} improvements. © 2022 IEEE.

关键词： Object detection

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Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

arXiv

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arXiv 2024年

作者： Liu, Zeyang Wan, Lipeng Yang, Xinrui Chen, Zhuoran Chen, Xingyu Lan, Xuguang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Application Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action-observation space. However, they often face challenges obtaining specific joint action sequences to reach successful states in long-horizon tasks. To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios. IIE employs a transformer model to imagine how the agents reach a critical state that can influence each other’s transition functions. Then, we initialize the environment at this state using a simulator before the exploration phase. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. The prompt consists of timestep-to-go, return-to-go, influence value, and one-shot demonstration, specifying the desired state and trajectory as well as guiding the action generation. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important under-explored regions. Despite its simplicity, empirical results demonstrate that our method outperforms multi-agent exploration baselines on the StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments. Particularly, IIE shows improved performance in the sparse-reward SMAC tasks and produces more effective curricula over the initialized states than other generative methods, such as CVAE-GAN and diffusion models. © 2024, CC BY.

关键词： Reinforcement learning

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Anti-Disturbance Path Following of Unmanned Surface Vehicles Based on Fuzzy Active Disturbance Rejection Control 5

Anti-Disturbance Path Following of Unmanned Surface Vehicles...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Ma, Chuang Wang, Wei Er, Meng Joo Gong, Huibin Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China College of Intelligent Systems Science Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

With the view of achieving path following of unmanned surface vehicles (USVs) in a complex marine environment, a novel path following algorithm based on fuzzy active disturbance rejection control (FADRC) is proposed. Firstly, an improved LOS (IMLOS) guidance law is designed deal with influences of sideslip angle on path following so as to improve robustness of the guidance system. Secondly, to circumvent the problem that the traditional ADRC method cannot automatically adjust system parameters, a FADRC controller which can realize disturbance compensation without accurate model parameters is designed. FADRC can estimate and eliminate internal and external disturbances. The IMLOS and FADRC are integrated to form a composite path following controller. The simulation results demonstrate that the proposed FADRC. © 2022 IEEE.

关键词： Unmanned surface vehicles

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Stimulating and harnessing circularly polarized luminescence of helically assembled carbonized polymer dots via interfacial dynamics

Aggregate

引用

Aggregate 2023年第4期4卷 100-110页

作者： Jiaying Lin Rulin Liu Peixian Chen Yangfei Lv Junjie Hao Meijuan Chen Dongxiang Zhang Ruikun Pan Yiwen Li Xi Zhu Tingchao He Jiaji Cheng School of Materials Science and Engineering Hubei UniversityWuhanHubeiChina College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhenGuangdongChina School of Science and Engineering The Chinese University of Hong KongShenzhen Institute of Artificial Intelligence and Robotics for Society(AIRS)ShenzhenGuangdongChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenGuangdongChina

Stimulating and harnessing circularly polarized luminescence(CPL)is not only a sine qua non for fundamentally unveiling chirogenesis in physical chemistry,but also a pivotal prerequisite for implementation of such phenomenon in research fields including chiral optoelectronics and ***,red-emissive carbonized polymer dots(CPDs)-based helical structures were synthesized in this work via biomolecule-tailored organic–inorganic co-assembly *** surface statesrelated chirality exhibits enhanced circular dichroism(CD)and CPL activities with anisotropic factors as high as gCD,max=5.4×10^(−3) and glum,max=1.5×10^(−2),*** obtained CPL signals can be further manipulated in an excitationdependent manner,indicating that a synergistic-competition phenomenon exists between configurational chirality and intermolecular energy-transfer dynamics,which is further supported by simulations based on density function theory(DFT).Such tunable CPL behavior triggers revolutionary designs and applications of these chiral CPDs into the realm of chirality-related biological issues and next-generation chiral optoelectronics.

关键词： carbonized polymer dots circular dichroism circularly polarized luminescence organic-inorganic coassembly synergistic-competition

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Towards combating frequency simplicity-biased learning for domain generalization 24

Towards combating frequency simplicity-biased learning for d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xilin He Jingyu Hu Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Muhammad Haris Khan Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guangdong Provincial Key Laboratory of Intelligent Information Processing University of Exeter Mohamed bin Zayed University of Artificial Intelligence

ISBN: (纸本)9798331314385

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at https://***/C0notSilly/AdvFrequency.

关键词：

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Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

arXiv

引用

arXiv 2024年

作者： Chen, Shitao Zhang, Haolin Zheng, Nanning The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi’an China

3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding. Nevertheless, an overlooked practical issue in existing methods is the ambiguity in training sample allocation based on box Intersection over Union (IoUbox). This problem impedes further enhancements in the performance of anchor-based LiDAR 3D object detectors. To tackle this challenge, this paper introduces a new training sample selection method that utilizes point cloud distribution for anchor sample quality measurement, named Point Assisted Sample Selection (PASS). This method has undergone rigorous evaluation on two widely utilized datasets. Experimental results demonstrate that the application of PASS elevates the average precision of anchor-based LiDAR 3D object detectors to a novel state-of-the-art, thereby proving the effectiveness of the proposed approach. The codes will be made available at https://***/XJTU-Haolin/Point_Assisted_Sample_Selection. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors

arXiv

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arXiv 2025年

作者： Zhao, Yi Zhang, Youzhi Department of Computing The Hong Kong Polytechnic University Hong Kong Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China

Large language models (LLMs) are widely used in real-world applications, raising concerns about their safety and trustworthiness. While red-teaming with jailbreak prompts exposes the vulnerabilities of LLMs, current efforts focus primarily on single-turn attacks, overlooking the multi-turn strategies used by real-world adversaries. Existing multi-turn methods rely on static patterns or predefined logical chains, failing to account for the dynamic strategies during attacks. We propose Siren, a learning-based multi-turn attack framework designed to simulate real-world human jailbreak behaviors. Siren consists of three stages: (1) training set construction utilizing Turn-Level LLM feedback (Turn-MF), (2) post-training attackers with supervised fine-tuning (SFT) and direct preference optimization (DPO), and (3) interactions between the attacking and target LLMs. Experiments demonstrate that Siren achieves an attack success rate (ASR) of 90% with LLaMA-3-8B as the attacker against Gemini-1.5Pro as the target model, and 70% with Mistral-7B against GPT-4o, significantly outperforming single-turn baselines. Moreover, Siren with a 7B-scale model achieves performance comparable to a multi-turn baseline that leverages GPT-4o as the attacker, while requiring fewer turns and employing decomposition strategies that are better semantically aligned with attack goals. We hope Siren inspires the development of stronger defenses against advanced multi-turn jailbreak attacks under realistic scenarios. Code is available at https://***/YiyiyiZhao/siren. Warning: This paper contains potentially harmful text. © 2025, CC BY-NC-SA.

关键词： Semantics

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Transosv: Offline Signature Verification with Transformers

SSRN

引用

SSRN 2022年

作者： Li, Huan Wei, Ping Ma, Zeyu Li, Changkai Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Signature verification is a frequently-used forensics technology in many safety related situations. Although the previous convolution neural network (CNN) based methods have made a great progress, the limitation of local neighborhood operation of CNN impedes reasoning relation of global context of signature strokes. To overcome this weakness, in this paper, we propose a novel holistic-part unified model named TransOSV based on the popular vision transformer framework to solve offline signature verification problem. The signature images are first encoded into patch sequences by the proposed transformer-based holistic encoder to learn the global signature representation. Second, considering the subtle local difference between the genuine signature and the forged signature, we design a novel contrast based part decoder along with a sparsity loss, which are utilized to learn the discriminative part features. With the learned holistic features and the part features, the proposed model is optimized by the contrast loss function. To reduce the influence of sample imbalance, we also formulate a new focal contrast loss function. Furthermore, we conduct the proposed model to learn signature representations for writer-dependent signature verification task. To the best of our knowledge, this is the first work to adopt a transformer structure for offline signature verification task. Our experimental results prove the potential of the proposed TransOSV model for both writer-independent and writer-dependent signature verification, which achieves state-of-the-art performance on four frequently-used offline signature datasets. © 2022, The Authors. All rights reserved.

关键词： Decoding

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Healthcare Monitoring Companion: The Social Robot

Healthcare Monitoring Companion: The Social Robot

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artificial intelligence For Internet of Things (AIIoT), International Conference on

作者： Rahul Goraksha Arunkumar Bongale Sameer Sayyad Satish Kumar Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Robotics and Automation Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide; during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature.

关键词： Temperature sensors Temperature measurement Ultrasonic variables measurement Social robots Prototypes Medical services Sensors

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