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IEEE International Conference on robotics and Automation (ICRA)

作者： Wen Fan Haoran Li Weiyong Si Shan Luo Nathan Lepora Dandan Zhang Department of Bioengineering Imperial College London School of Engineering Mathematics and Technology and Bristol Robotics Laboratory University of Bristol School of Computer Science and Electronic Engineering University of Essex Department of Engineering King’s College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Tactile sensing is significant for robotics since it can obtain physical contact information during manipulation. To capture multimodal contact information within a compact framework, we designed a novel sensor called ViTacTip, which seamlessly integrates both tactile and visual perception capabilities into a single, integrated sensor unit. ViTacTip features a transparent skin to capture fine features of objects during contact, which can be known as the see-through-skin mechanism. In the meantime, the biomimetic tips embedded in ViTacTip can amplify touch motions during tactile perception. For comparative analysis, we also fabricated a ViTac sensor devoid of biomimetic tips, as well as a TacTip sensor with opaque skin. Furthermore, we develop a Generative Adversarial Network (GAN)-based approach for modality switching between different perception modes, effectively alternating the emphasis between vision and tactile perception modes. We conducted a performance evaluation of the proposed sensor across three distinct tasks: i) grating identification, ii) pose regression, iii) contact localization and force estimation. In the grating identification task, ViTacTip demonstrated an accuracy of 99.72%, surpassing TacTip, which achieved 94.60%. It also exhibited superior performance in both pose and force estimation tasks with the minimum error of 0.08 mm and 0.03N, respectively, in contrast to ViTac’s 0.12 mm and 0.15N. Results indicate that ViTacTip outperforms single-modality sensors.

关键词： Biomimetics Force Estimation Switches Robot sensing systems Skin Sensors

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Vision Language Model Empowered Surgical Planning

Vision Language Model Empowered Surgical Planning

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Yihe Chen Runsheng Yu Xin Wang Wensheng Wang Ning Tan Youzhi Zhang School of Artificial Intelligence Nanjing University Nanjing China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

The integration of a flexible endoscope with a surgical manipulator is crucial in minimally invasive surgery (MIS), facilitating detailed visualization of the operative field within the patient’s body. During MIS, the remote center of motion (RCM) constraints are essential for achieving visual servoing control and ensuring accurate tracking control of the robotic endoscope. Existing work requires the exact trajectory for the tracking control and does not connect both tasks with the RCM constraints. In this paper, we exploit GPT-V to develop Vision Language Model Empowered surgical Planning (VLM-EP), which uses environmental observations and task description to finish the tracking task without the exact trajectory and connect both tasks through the exploration procedure in vivo safety range. Our simulated experiments show that our VLM-EP significantly outperforms the state-of-the-art control-based baseline. We demonstrate a practical implementation of VLM-EP in real-world scenarios, which shows that VLM-EP effectively handles the tracking control task and the visual servoing control task.

关键词： Visualization In vivo Minimally invasive surgery Tracking Endoscopes Manipulators Visual servoing Planning Trajectory Safety

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Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

Outram: One-shot Global Localization via Triangulated Scene ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Pengyu Yin Haozhi Cao Thien-Minh Nguyen Shenghai Yuan Shuyang Zhang Kangcheng Liu Lihua Xie Centre for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://***/Pamphlett/Outram.

关键词： Location awareness Point cloud compression Three-dimensional displays Laser radar Costs Pose estimation Feature extraction

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Byzantine Robust Aggregation in Federated Distillation with Adversaries

Byzantine Robust Aggregation in Federated Distillation with ...

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International Conference on Distributed Computing Systems

作者： Wenrui Li Hanlin Gu Sheng Wan Zhirong Luan Wei Xi Lixin Fan Qiang Yang Badong Chen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China WeBank AI Lab Shenzhen China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China School of Electrical Engineering Xi'an University of Technology Xi'an China Department of Computer Science Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated learning empowers privacy-preserving, multi-party secure model training without the necessity of sharing raw data. In recent years, knowledge distillation has emerged as a promising solution to address the significant challenge of model heterogeneity within federated learning. However, current research often overlooks the potential threats posed by Byzantine attacks, which can significantly compromise the security of federated distillation. Previous work on Byzantine attacks has been primarily focused on manipulating local gradients to compromise global model, lacking attacks on logits in knowledge distillation scenarios. In this paper, we introduce two innovative attacks, shedding light on the inherent risks in federated distillation. The proposed attacks include a top-k attack, which perturbs the top k values of logits in each column, and an impersonation attack, which emulates knowledge significantly deviating from the norm. To counter such attacks, we propose a robust aggregation strategy-FedTGD (Federated Top Guard Distillation), designed to ensure robust distillation with heterogeneous models. Specifically, FedTGD incorporates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and maximum cosine similarity on top-k values of logits to select benign knowledge. Experimental evaluations conducted on FEMNIST and CIFAR100 datasets, considering scenarios for both IID and Non-IID, reveal that top-k attack results in a substantial 27.16% accuracy reduction for FedMD. In contrast, our aggregation method shows a marginal 0.7% accuracy decrease under top-k attacks, outperforming state-of-the-art baselines.

关键词： Training Accuracy Federated learning Computational modeling Noise Impersonation attacks Data models

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Privacy Preserving Charge Location Prediction for Electric Vehicles

arXiv

引用

arXiv 2025年

作者： Marlin, Robert Jurdak, Raja Abuadbba, Alsharif Miller, Dimity School of Computer Science Australia School of Electrical Engineering and Robotics Queensland University of Technology Australia CSIRO’s Data61 Cyber Security Cooperative Research Centre Australia CSIRO’s Data61 Australia

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs’ next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN’s ability to forecast energy demands over extended periods. We present a privacy focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks. © 2025, CC BY-NC-ND.

关键词： Differential privacy

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Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone 28th

Ghost Swarms: Learning Swarm Rules from Environmental Chang...

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28th European Conference on Genetic Programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Alharthi, Khulud Abdallah, Zahraa S. Hauert, Sabine Bristol Robotics Laboratory University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia

ISBN: (纸本)9783031899904

Swarm behaviours emerge from agents interacting with their local environment following simple rules. While directly observing each agent can be challenging, their collective behaviour leaves detectable environmental imprints that could offer insights into the underlying swarm dynamics. However, this task is complex due to the hidden and interconnected relationships between the rules governing agent interactions, the emergent swarm behaviour, and the environmental changes generated by this behaviour. In this work, we propose a method for extracting human-readable controllers from demonstrations showing only observable environmental imprints caused by the swarm. This approach explores whether these environmental imprints can reveal the swarm’s actions, even when the individual agents are challenging to track. Our approach eliminates the need for prior knowledge about the controller or its structure, enabling the successful learning of controllers from a single demonstration. We provide a novel method for understanding and managing both natural and engineered swarms by utilising the environmental imprints left by swarm behaviours, even when direct observation of the swarm’s actions is not feasible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental Imprints Imitation Learning Swarm Behaviour

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A Study of Noise Reduction Algorithm Using Statistical Optimization in Digital Holographic Microscopy

A Study of Noise Reduction Algorithm Using Statistical Optim...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Jongpil Jeong Hyun–Woo Kim Myungjin Cho Min–Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan School of ICT Robotics and Mechanical Engineering IITC. Hankyong National University Anseong Kyonggi-do Republic of Korea

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Recently, due to the coronavirus era, we have a heightened need for fast and accurate diagnostics. To meet these requirements, there are many methods such as polymerase chain reaction, but they also require about from a few hours to a day. In contrast, a three-dimensional model of cells can be observed by simply extracting cells and photographing them with digital holographic microscopy, and in the case of diseases that can be classified b y t he shape of cells, diagnosis i s possible in a few minutes. However, there are precise focal issues and noise in the high-frequency domain, respectively. Therefore, to resolve these issues, we propose an optimization method by modifying the threshold value of the high variance pixel averaging method in digital holographic microscopy.

关键词： Gamma distribution Visualization Three-dimensional displays Shape Microscopy Noise Noise reduction

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Delving into Decision-based Black-box Attacks on Semantic Segmentation

arXiv

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arXiv 2024年

作者： Chen, Zhaoyu Shan, Zhengyang Chang, Jingwen Jiang, Kaixun Yang, Dingkang Cheng, Yiting Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China

Semantic segmentation is a fundamental visual task that finds extensive deployment in applications with security-sensitive considerations. Nonetheless, recent work illustrates the adversarial vulnerability of semantic segmentation models to white-box attacks. However, its adversarial robustness against black-box attacks has not been fully explored. In this paper, we present the first exploration of black-box decision-based attacks on semantic segmentation. First, we analyze the challenges that semantic segmentation brings to decision-based attacks through the case study. Then, to address these challenges, we first propose a decision-based attack on semantic segmentation, called Discrete Linear Attack (DLA). Based on random search and proxy index, we utilize the discrete linear noises for perturbation exploration and calibration to achieve efficient attack efficiency. We conduct adversarial robustness evaluation on 5 models from Cityscapes and ADE20K under 8 attacks. DLA shows its formidable power on Cityscapes by dramatically reducing PSPNet’s mIoU from an impressive 77.83% to a mere 2.14% with just 50 queries. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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ClickVOS: Click Video Object Segmentation

arXiv

引用

arXiv 2024年

作者： Guo, Pinxue Hong, Lingyi Zhou, Xinyu Gao, Shuyong Li, Wanyun Li, Jinglun Chen, Zhaoyu Li, Xiaoqiang Zhang, Wei Zhang, Wenqiang the Shanghai Engineering Research Center of AI&Robotics Academy for Engineering&Technology Fudan University Shanghai China the Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China the School of Computer Engineering and Science Shanghai University Shanghai China Engineering Research Center of AI&Robotics Ministry of Education Academy for Engineering&Technology Fudan University Shanghai China

—Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame during inference or lack the flexibility to specify arbitrary objects of interest. To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame. And we provide the extended datasets DAVIS-P and YouTubeVOS-P that with point annotations to support this task. ClickVOS is of significant practical applications and research implications due to its only 1-2 seconds interaction time for indicating an object, comparing annotating the mask of an object needs several minutes. However, ClickVOS also presents increased challenges. To address this task, we propose an end-to-end baseline approach named called Attention Before Segmentation (ABS), motivated by the attention process of humans. ABS utilizes the given point in the first frame to perceive the target object through a concise yet effective segmentation attention. Although the initial object mask is possibly inaccurate, in our ABS, as the video goes on, the initially imprecise object mask can self-heal instead of deteriorating due to error accumulation, which is attributed to our designed improvement memory that continuously records stable global object memory and updates detailed dense memory. In addition, we conduct various baseline explorations utilizing off-the-shelf algorithms from related fields, which could provide insights for the further exploration of ClickVOS. The experimental results demonstrate the superiority of the proposed ABS approach. Extended datasets and codes will be available at https://***/PinxueGuo/ClickVOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion compensation

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PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving

arXiv

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arXiv 2024年

作者： Fu, Jiyuan Chen, Zhaoyu Jiang, Kaixun Guo, Haijing Gao, Shuyong Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University China

Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, posing significant risks to the reliability and safety of autonomous vehicles. Adversaries can exploit these vulnerabilities to manipulate the vehicle’s perception of its surroundings, leading to erroneous decisions and potentially catastrophic consequences. To address this challenge, we propose a novel Precision-Guided Adversarial Attack (PG-Attack) framework that combines two techniques: Precision Mask Perturbation Attack (PMP-Attack) and Deceptive Text Patch Attack (DTP-Attack). PMP-Attack precisely targets the attack region to minimize the overall perturbation while maximizing its impact on the target object’s representation in the model’s feature space. DTP-Attack introduces deceptive text patches that disrupt the model’s understanding of the scene, further enhancing the attack’s effectiveness. Our experiments demonstrate that PG-Attack successfully deceives a variety of advanced multi-modal large models, including GPT-4V, Qwen-VL, and imp-V1. Additionally, we won First-Place in the CVPR 2024 Workshop Challenge: Black-box Adversarial Attacks on Vision Foundation Models‡ and codes are available at https://***/fuhaha824/PG-Attack. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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