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Yolov5 Outdoor Dynamic Object Detection Based on Multi-scale Feature Fusion 1

17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Yu, Chengfeng Chen, Ken Jiang, Lin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

Outdoor object target detection is a very popular research task. 7 categories of data that fit the outdoor scenario were selected from the VOC2012 dataset as the current dataset for this study. In order to improve the detection accuracy of outdoor objects while keeping the network in good real-time, we propose a new yolov5 outdoor object detection network structure based on multi-scale feature fusion, which fuses shallow and deep features at different scales so that the fused feature layer has rich semantic information while enhancing the positioning information. The MAP of its proposed network on the VOC2012 dataset improved by 1.5% compared to Yolov5, and the FPS reached 61.51, which is better than the original Yolov5 method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Arbitrarily Scalable Environment Generators via Neural Cellular Automata 37

Arbitrarily Scalable Environment Generators via Neural Cellu...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhang, Yulun Fontaine, Matthew C. Bhatt, Varun Nikolaidis, Stefanos Li, Jiaoyang Robotics Institute Carnegie Mellon University United States Thomas Lord Department of Computer Science University of Southern California United States

ISBN: (纸本)9781713899921

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2, 350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns. We include the source code at https://***/lunjohnzhang/warehouse_env_gen_nca_public. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Multipurpose robots

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3D Ball Trajectory Reconstruction of a Ballistic Shot from a Monocular Basketball Video 49

3D Ball Trajectory Reconstruction of a Ballistic Shot from a...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chao, Vanyi Jamsrandorj, Ankhzaya Oo, Yin May Mun, Kyung-Ryoul Kim, Jinwook University of Science and Technology Ai Robotics Daejeon34113 Korea Republic of University of Science and Technology Human Computer Interface and Robotics Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

3D ball location in sports can be advantageous and has many applications such as automatic scoring system, ball trajectory highlight and offside detection system for football game. Currently, this task can be solved through the use of costly multi-view approaches or the legacy physics-based methods that require complicated setups and human intervention. In this paper, we propose a method to reconstruct the 3D ball trajectory of the basketball projectile shot by using only a single view. The method relies on accurate camera calibration and 2D ball detection of sequence images. Extra information in the 3D world also plays an important role in improving the 3D reconstruction. The experimental result illustrates the accurately reconstructed 3D trajectory when enough information is given. © 2023 IEEE.

关键词： Ballistics

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A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration 49

A Residual Attention-based EfficientNet Homography Estimatio...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Oo, Yin May Jamsrandorj, Ankhzaya Chao, Vanyi Mun, Kyung-Ryoul Kim, Jinwook Ai Robotics University of Science and Technology Daejeon34113 Korea Republic of Human Computer Interface and Robotics University of Science and Technology Daejeon34113 Korea Republic of Center for Artificial Intelligence Korea Institute of Science and Technology Seoul02792 Korea Republic of

ISBN: (纸本)9798350331820

Accurate sports field registration plays a crucial role in computer vision for effective team sports analysis, as it provides the ability to track and recognize the positions, movements, and interactions of players from image space to real-world field space. In this paper, we propose a two-stage deep learning framework for sports field registration. In the first-stage network, we utilize a Residual EfficientNet-Attention U-Net architecture to estimate the initial homography matrix using predefined keypoints to register sports fields. Subsequently, our second-stage network refines the initial homography for improved accuracy. We evaluate our proposed method on the public sports datasets to show its outperform results against the state-of-the-art methods in some evaluation metrics. © 2023 IEEE.

关键词： Sports

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Position: scaling simulation is neither necessary nor sufficient for in-the-wild robot manipulation 24

Position: scaling simulation is neither necessary nor suffic...

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Proceedings of the 41st International Conference on Machine Learning

作者： Homanga Bharadhwaj The Robotics Institute School of Computer Science Carnegie Mellon University

In this paper, we develop a structured critique of robotic simulations for real-world manipulation, by arguing that scaling simulators is neither necessary nor sufficient for making progress in general-purpose real-world robotic manipulation agents that are compliant with human preferences. With the ubiquity of robotic simulators, and recent efforts to scale them for diverse tasks, and at the same time the interest in generally capable real-world manipulation systems, we believe it is important to address the limitations of using simulation for real-world manipulation, so that as a community, we can focus our collective resources, energy, and time on approaches that have more principled odds of success. We further demonstrate the unique challenges that real-world manipulation presents, and show through examples and arguments why scaling simulation doesn't get us closer to solving these challenges required for diverse real-world deployment.

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Motion planning for robotics:A review for sampling-based planners

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Biomimetic Intelligence & robotics 2025年第1期5卷 15-34页

作者： Liding Zhang Kuanqi Cai Zewei Sun Zhenshan Bing Chaoqun Wang Luis Figueredo Sami Haddadin Alois Knoll School of Computation Information and Technology(CIT)Technical University of MunichMunich 85748Germany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunich 80992Germany School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science University of NottinghamNottingham 315199United Kingdom

Recent advancements in robotics have transformed industries such as manufacturing,logistics,surgery,and planetary exploration.A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length,sweep area,execution time,and energy *** the available algorithms,sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments,explore free space,and offer probabilistic completeness along with other formal *** their widespread application,significant challenges still *** advance future planning algorithms,it is essential to review the current state-of-the-art solutions and their *** this context,this work aims to shed light on these challenges and assess the development and applicability of sampling-based ***,we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various *** findings highlight the strides made in sampling-based methods while underscoring persistent *** work offers an overview of the important ongoing research in robotic motion planning.

关键词： robotics Motion planning Sampling-based algorithms

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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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2024 IEEE Calcutta Conference, CALCON 2024

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

ISBN: (纸本)9798331516666

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. © 2024 IEEE.

关键词： Encryption algorithms

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Uncertainty-aware Condensation for OCT Cervical Dataset Combining Coreset Selection and Distillation

Uncertainty-aware Condensation for OCT Cervical Dataset Comb...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiong, Yuxuan Xu, Yongchao Zhang, Yan Gui, Kai Du, Bo School of Computer Science Wuhan University Wuhan China Renmin Hospital Wuhan University Department of Obstetrics and Gynecology Wuhan China Yijiahe Robotics Research Institute Nanjing China

ISBN: (纸本)9798350386226

Optical Coherence Tomography (OCT) cervical images offer a sensitive and non-invasive solution for screening cervical cancer. Currently, utilizing deep learning techniques to interpret these images has emerged as a promising approach for computer-aided diagnosis. However, challenges in computational efficiency and privacy protection persist in this field. To address these issues, we propose an uncertainty-aware filtering before distillation (UFD) framework to compress the OCT cervical dataset in a trustworthy and efficient manner. Our approach involves pre-training an uncertainty proxy to evaluate the importance of each sample, allowing us to extract a valuable subset (i.e., coreset) from the original dataset. We then apply importance-weighted distribution matching for dataset distillation. The distribution matching loss is calculated as the distance between the class centers of the uncertainty-weighted coreset and the synthetic distilled dataset. Extensive experiments validate the feasibility and effectiveness of our proposed UFD framework. It is worth noting that after selecting the most representative samples from the original OCT dataset through coreset selection, the performance of the trained network is comparable to, or sometimes even slightly higher than, that of the network trained with all the original data. This indicates that automated dataset compression and cleaning techniques are crucial for improving image classification problems in medical imaging datasets, such as OCT and ultrasound, which are rich in noise and erroneous samples. © 2024 IEEE.

关键词： Optical coherence tomography

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Finding Optimal Nash Equilibria in Multiplayer Games via Correlation Plans 22

Finding Optimal Nash Equilibria in Multiplayer Games via Cor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Zhang, Youzhi An, Bo Subrahmanian, V.S. Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Northwestern University Evanston United States

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we propose a novel algorithm called CRM based on a novel mixed-integer bilinear program with correlation plans for some subsets of players, which uses Correlation plans with their Relations to strictly reduce the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to significantly reduce the number of bilinear terms. Experimental results show that our algorithm can be several orders of magnitude faster than the state-of-the-art baseline. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Computation theory

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