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IEEE International Conference on robotics and Automation (ICRA)

作者： Ran Qin Haoxiang Ma Boyang Gao Di Huang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the geometry information recorded in the depth channel, a complete 6-dimensional rectangle representation is adopted with the grasp depth dedicatedly considered in addition to those defined in the common 5-dimensional one. The prediction of the extra grasp depth substantially strengthens feature learning, thereby leading to more accurate results. Moreover, to reduce the negative impact caused by the discrepancy of data quality in two modalities, a Local Cross-modal Attention (LCA) module is designed, where the depth features are refined according to cross-modal relations and concatenated to the RGB ones for more sufficient fusion. Extensive simulation and physical evaluations are conducted and the experimental results highlight the superiority of the proposed approach.

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Quantum Quest: Resource Requirement of RC4 Cryptanalysis under Grover’s Lens

Quantum Quest: Resource Requirement of RC4 Cryptanalysis und...

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IEEE Calcutta Conference (CALCON)

作者： Sarbani Sen Soumen Bajpayee Imon Mukherjee Goutam Paul Bhupendra Singh Computer Science & Engineering Indian Institute of Information Technology Kalyani India Cryptology & Security Research Unit Indian Statistical Institute Kolkata India Centre for Artificial Intelligence and Robotics DRDO Bengaluru India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

The RC4 cipher is a widely used software stream cipher which has long stood as a cornerstone in cryptographic applications due to its design simplicity, speed and efficiency. In this paper, we present an approach to decrypt RC4-encrypted data by leveraging Grover’s quantum search algorithm. To the best of our knowledge, this is the first documented attempt to employ Grover’s algorithm in breaking RC4 encryption. We enumerate the quantum resources required to perform exhaustive key search attack on RC4. Our approach demonstrates the potential of quantum computing to revolutionize cryptanalysis.

关键词： Ciphers Costs Quantum computing Software algorithms NIST Software Encryption Optimization Faces Lenses

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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1817-1826页

作者： Gaofeng Li Dezhen Song Lei Sun Shan Xu Hongpeng Wang Jingtai Liu Nankai University Tianjin 300350China Humanoids and Human Centered Mechatronics Research Line Italian Institute of Technology16163 GenovaItaly Department of Computer Science and Engineering Texas A&M UniversityCollege StationTexas 77843 USA Institute of Robotics and Automatic Information System Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable *** deformable link is composed of passive spherical joints with preload forces between socket-ball *** estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable *** yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction *** further develop a parameter estimation method to estimate the product of preload force and static friction *** experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

关键词： Deformable link parameters identification spherical joint with preload force static force-based model

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Focused Bidirectional Search Trees: A Bidirectional Optimal Fast Matching Method for Robot Path Planning

Focused Bidirectional Search Trees: A Bidirectional Optimal ...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Li, Chenming Wang, Jiankun Meng, Max Q.-H. The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Southern University of Science and Technology Jiaxing Research Institute Jiaxing China University of Alberta Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350327182

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT∗ can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT∗ can meet at any portion of the path. FBFMT∗ reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces. © 2023 IEEE.

关键词： Robot programming

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models

arXiv

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arXiv 2025年

作者： Spravil, Julian Houben, Sebastian Behnke, Sven Fraunhofer IAIS Germany University of Applied Sciences Bonn-Rhein-Sieg Bonn Germany University of Bonn Computer Science Institute VI Center for Robotics Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Cross-lingual transfer enables vision-language models (VLMs) to perform vision tasks in various languages with training data only in one language. Current approaches rely on large pre-trained multilingual language models. However, they face the curse of multilinguality, sacrificing downstream task performance for multilingual capabilities, struggling with lexical ambiguities, and falling behind recent advances. In this work, we study the scaling laws of systematic generalization with monolingual VLMs for multilingual tasks, focusing on the impact of model size and seen training samples. We propose Florenz, a monolingual encoder-decoder VLM with 0.4B to 11.2B parameters combining the pre-trained VLM Florence-2 and the large language model Gemma-2. Florenz is trained with varying compute budgets on a synthetic dataset that features intentionally incomplete language coverage for image captioning, thus, testing generalization from the fully covered translation task. We show that not only does indirectly learning unseen task-language pairs adhere to a scaling law, but also that with our data generation pipeline and the proposed Florenz model family, image captioning abilities can emerge in a specific language even when only data for the translation task is available. Fine-tuning on a mix of downstream datasets yields competitive performance and demonstrates promising scaling trends in multimodal machine translation (Multi30K, CoMMuTE), lexical disambiguation (CoMMuTE), and image captioning (Multi30K, XM3600, COCO Karpathy). Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Neelesh Kumar Sahu Piyush Chaturvedi Vaishali Gupta Ruchi Patel Ashok Verma Megha Shrivastava Artificial Intelligence and Robotics Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Computer Sc. & Engg. IPS Academy Institute of Engineering & Science Indore MP India Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences Jabalpur MP India Department of Mathematics Medicaps University Indore MP India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks.

关键词： Fabrication Remotely guided vehicles Automation Solids Sensors Maintenance Information and communication technology Servomotors Logistics

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Extinguishing Real Fires by Fully Autonomous Multirotor UAVs in the MBZIRC 2020 Competition

Field Robotics

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Field robotics 2025年 2卷 406-436页

作者： Viktor Walter Vojtěch Spurný Matěj Petrlík Tomáš Báča David Žaitlík Lyubomyr Demkiv Martin Saska Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague http://mrs.felk.cvut.cz Robotics Group of SoftServe Inc. and with the Institute of Computer Science and Information Technologies Lviv Polytechnic National University Ukraine

In this paper, we describe a system for combating real fires with sprayed liquid extinguishing agent using a team of multirotor UAVs. The system design relies on onboard sensors and operates without the need for human intervention throughout its complex mission, from its takeoff to landing. The core UAV platform can estimate its state, self-localize, navigate and locate and extinguishing fires. Thermal and RGB cameras are used, each with a specialized computer vision subsystem and are combined with planar LIDAR for 3D localization of fires on multistory building facades. The system conducts aerial firefighting with a software stack that addresses flight dynamics and sensor limitations and a liquid-spraying subsystem incorporating a rigidly-attached water nozzle. The approach presented in this paper was motivated by the Mohamed Bin Zayed International robotics Challenge (MBZIRC 2020) firefighting scenario, which focused on coordinated multi-UAV teams that can autonomously combat high-rise building fires. The MBZIRC series places particular emphasis on fast and reliable deployment of robots in realistic conditions. These contests promote development of real-world applications that are greatly needed by society, but which still exceed State-of-the-Art in the robotics community. To our knowledge, our system was the only MBZIRC 2020 contender to extinguish a facade fire successfully in autonomous mode without using an RTK-GNSS system. Our approach contributed to victory in the overall competition and we have now adapted it into an industrial prototype for a firefighting UAV. A video attachment to this paper is available at http://***/fr2020firechallenge-facadefires.

关键词： Robots Buildings Autonomous aerial vehicles Location awareness Robot kinematics Inspection Navigation Cameras Monitoring Sensor systems

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang 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 National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers

arXiv

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arXiv 2023年

作者： Yi, Yuhao You, Ronghui Liu, Hong Liu, Changxin Wang, Yuan Lv, Jiancheng College of Computer Science Sichuan University China School of Statistics and Data Science Nankai University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden School of Robotics Hunan University China

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. This paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. The proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers. Code and appendix are available at https://***/jerry907/AAAI24-RASHB. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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