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IEEE International Conference on Robotics and Automation (ICRA)

作者： Weizheng Wang Le Mao Ruiqi Wang Byung-Cheol Min Department of Computer and Information Technology SMART Laboratory Purdue University West Lafayette IN USA College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In public spaces shared with humans, ensuring multi-robot systems navigate without collisions while respecting social norms is challenging, particularly with limited communication. Although current robot social navigation techniques leverage advances in reinforcement learning and deep learning, they frequently overlook robot dynamics in simulations, leading to a simulation-to-reality gap. In this paper, we bridge this gap by presenting a new multi-robot social navigation environment crafted using Dec-POSMDP and multi-agent reinforcement learning. Furthermore, we introduce SAMARL: a novel benchmark for cooperative multi-robot social navigation. SAMARL employs a unique spatial-temporal transformer combined with multi-agent reinforcement learning. This approach effectively captures the complex interactions between robots and humans, thus promoting cooperative tendencies in multi-robot systems. Our extensive experiments reveal that SAMARL outperforms existing baseline and ablation models in our designed environment. Demo videos for this work can be found at: https://***/view/samarl

关键词： Navigation Rail to rail inputs Human-robot interaction Reinforcement learning Benchmark testing Transformers Multi-robot systems

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Investigating the Role of AI in Personalized Consumer Banking Experiences

Investigating the Role of AI in Personalized Consumer Bankin...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Rohella, Piyush Temara, Sheetal Samanthapudi, Sagar Varma Gupta, Ketan Suganyadevi, K. Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

This research examines the function of synthetic Intelligence in developing customized purchaser banking reviews. Artificial Intelligent -based total gear is increasingly being followed to offer consumers customized banking reports. Studies into the use of AI in this context will tell the layout of future banking products and services. The primary intention of this research is to understand the modern technological and personal adoption developments related to Artificial Intelligent and customized banking reviews. Particularly, this study will identify the modern use cases of Artificial Intelligent in personalized banking, look into clients' attitudes and motivations towards Artificial Intelligent -enabled banking experiences, and discover the ability possibilities and challenges related to utilizing Artificial Intelligent in growing individualized banking studies for purchasers. Additionally, the role of ethical issues in implementing and the use of Artificial Intelligent in personalized consumer banking experiences may be evaluated. This study is crucial for presenting a better understanding of the function of Artificial Intelligent in developing personalized and moral consumer banking experiences. © 2024 IEEE.

关键词： Motivation

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Implications of Personality on Cognitive Workload, Affect, and Task Performance in Remote Robot Control

arXiv

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arXiv 2023年

作者： Cha, Go-Eum Jo, Wonse Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

This paper explores how the personality traits of robot operators can influence their task performance during remote control of robots. It is essential to explore the impact of personal dispositions on information processing, both directly and indirectly, when working with robots on specific tasks. To investigate this relationship, we utilize the open-access multi-modal dataset MOCAS to examine the robot operator's personality traits, affect, cognitive load, and task performance. Our objective is to confirm if personality traits have a total effect, including both direct and indirect effects, that could significantly impact the performance levels of operators. Specifically, we examine the relationship between personality traits such as extroversion, conscientiousness, and agreeableness, and task performance. We conduct a correlation analysis between cognitive load, self-ratings of workload and affect, and quantified individual personality traits along with their experimental scores. The findings show that personality traits do not have a total effect on task performance. A supplementary video can be accessed at: https://***/h3XUtVn7nzg. © 2023, CC BY-NC-ND.

关键词： Robots

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Contextual Reinforcement Learning for Unsupervised Deformable Multimodal Medical Images Registration

Contextual Reinforcement Learning for Unsupervised Deformabl...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Yang Zheng Hongjiang Xian Zhikun Shuai Jing Hu Xin Wang Shu Hu Department of Computer Science Chengdu University of Information Technology P.R. China Department of Epidemiology and Biostatistics School of Public Health University at Albany State University of New York (SUNY) NY USA Department of Computer and Information Technology Purdue University IN USA

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Multimodal deformable image registration refers to the process of finding the spatial correspondence between pairs of images with multimodal and mapping them onto the same coordinate system. Most of the deep learning-based registration methods are one-shot registration, which is difficult to handle images with significant deformations or displacements. Reinforcement learning can handle these challenges by viewing registration as a strategic decision-making process which is a step-by-step registration. However, it faces challenges with high-dimensional and continuous deformation fields. To overcome this, we introduce a planner network that maps high-dimensional input state to low-dimensional plan, guiding the actor to generate continuous actions. In order to handle complex multi-modal registration, we propose a multi-frame plan module which encourages artificial agent to explicitly utilize the redundant states in the registration process and learn more accurate registration actions from the generated state frames. To facilitate the training and convergence of the model, we define an unsupervised reward function and incorporate spectral normalization layers. The entire framework is a fully unsupervised registration framework and training in an end-to-end manner. We evaluated our method on publicly available T1w and T2w brain datasets, and the results indicate that our method has excellent deformable registration capability for multimodal images.

关键词： Training Image registration Visualization Accuracy Deformation Decision making Reinforcement learning Color Mutual information Biomedical imaging

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SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models

arXiv

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arXiv 2023年

作者： Kannan, Shyam Sundar Venkatesh, Vishnunandan L.N. Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models (LLMs), harnesses the power of LLMs to convert high-level task instructions provided as input into a multi-robot task plan. It accomplishes this by executing a series of stages, including task decomposition, coalition formation, and task allocation, all guided by programmatic LLM prompts within the few-shot prompting paradigm. We create a benchmark dataset designed for validating the multi-robot task planning problem, encompassing four distinct categories of high-level instructions that vary in task complexity. Our evaluation experiments span both simulation and real-world scenarios, demonstrating that the proposed model can achieve promising results for generating multi-robot task plans. The experimental videos, code, and datasets from the work can be found at https://***/view/smart-llm/. © 2023, CC BY.

关键词： Robot programming

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Skin Cancer Prediction Using CNN-Based Decision Fusion with Dermoscopic Images

Skin Cancer Prediction Using CNN-Based Decision Fusion with ...

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computer Science, computer Engineering, & Applied Computing (CSCE), Congress in

作者： Saeka Rahman Sudip Vhaduri Asheka Rahman Miad Faezipour School of Engineering Technology Electrical and Computer Engineering Technology Purdue University West Lafayette IN USA Department of Computer and Information Technology Purdue University West Lafayette IN USA Department of Computer Electronics and Graphic Technology Central Connecticut State University New Britain CT USA

Skin cancer is a type of cancer that develops in the cells of the skin and can affect any part of the body. Early-stage detection is crucial for the effective treatment and curability of this cancer. Skin cancer diagnosis includes visual screening, dermoscopic image analysis, biopsy, and histopathological examination. The accuracies of these diagnostic procedures are highly dependent on the experience of dermatologists. However, the number of experts in this field is limited. Therefore, automatic skin cancer detection from skin lesion images is an increasingly important field that can aid in medical research. This research proposes a Convolutional Neural Network (CNN) architecture-based decision-level fusion model to predict skin cancer from dermoscopic images with improved accuracy. In so doing, the research uses majority voting decision fusion to combine three individual CNN models. The models are validated using the International Skin Imaging Collaboration (ISIC) 2018 challenges dataset available in the ISIC archive to predict binary and multiclass skin cancer with validation accuracy of 0.83±0.01 and 0.91±0.01, respectively. Paired t-tests are conducted to test the difference in mean accuracies between the fused and best-performing individual models. The test results show that the fused models' mean accuracy is significantly better at a 5% significance level than the best individual models for both cases. The performance of the binary fused model is further compared to several state-of-the-art models and is found to outperform most of them, suggesting the considerable clinical significance.

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Uncovering the Benefits of Machine Learning for Automating Financial Regulatory Tasks

Uncovering the Benefits of Machine Learning for Automating F...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Samanthapudi, Sagar Varma Rohella, Piyush Temara, Sheetal Gupta, Ketan Dhanasekaran, S. The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in terms of price savings and more green process control. Current advances in ML have enabled the improvement of models that may interpret complicated sets of facts and automate the technique of extracting the preferred facts from these records. The development of such models gives a price-effective and efficient opportunity to greater luxurious and time-consuming guide labor. Moreover, these fashions are capable of perceiving and correctly categorizing data to ensure information accuracy and compliance with regulatory policies. It allows the company to adhere to policies without human oversight, lowering each value and time constraints. It will be further prolonged to other factors of the enterprise by making use of unsupervised fashions and fashions advanced on switch learning, together with deep learning. Those models allow the automation of responsibilities inclusive of assessing the threat associated with investments or transactions, facilitating the development of quicker and more knowledgeable techniques. Ordinary, the usage of ML fashions for financial law automatization processes is precious for decreasing price and time constraints and making sure of compliance. Through leveraging the ability of ML, financial institutions can take advantage of extra internal manner efficiency and statistics accuracy with fewer risks. © 2024 IEEE.

关键词： Deep learning

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Predicting unreliable response patterns in smartphone health surveys: A case study with the mood survey

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Smart Health 2023年 28卷

作者： Vhaduri, Sudip Cho, Jaea Meng, Kexin Computer and Information Technology Purdue University West Lafayette 47907 IN United States

Due to the advancement of smartphone sensing and computing capabilities, healthcare providers are increasingly relying on smartphone-based self-reported surveys to gather information regarding health and well-being for the purpose of analyzing various factors and their impact on people's health and well-being. However, the analysis could be misleading if the collected data is not reliable, which can happen due to several reasons, including lack of attention while taking the surveys. There is a dearth of knowledge about the reliability of smartphone-based health and well-being surveys. In this work, we present a case study with 86 participants responding to the Positive and Negative Affect Schedule (PANAS) mood survey twice — first unreliably and then carefully. From our detailed analysis with a set of 10 influential features, we are able to detect an unreliable submission with an accuracy of up to 0.93 using cross-validation. Findings from this work can be taken as guidelines for future research. © 2023

关键词： Machine learning Mobile health app Mood surveys Smartphone surveys Survey response patterns

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Cost-Efficient and Effective Counter Unmanned Aerial System via Visual-Acoustic Sensing

Cost-Efficient and Effective Counter Unmanned Aerial System ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Changyeon Lee Dongju Yu Soyeon Cho Dane W. Hindsley Halaevalu F. Patterson Megan A. Clecak Eric T. Matson Computer Science and Engineering Yonsei University Seoul Republic of Korea Computer Software Kunsan National University Gunsan Republic of Korea Software Convergence Kyung Hee University Yongin Republic of Korea Computer and Information Technology Purdue University West Lafayette IN USA Robotics Engineering Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

As Unmanned Aerial Vehicles (UAVs) become more accessible to the public, they become a common tool for malicious purposes. As a result, there is an increasing demand for Counter Unmanned Aerial Systems (CUAS) that can detect UAVs. Existing CUAS solutions often rely on high-priced radar systems or advanced technologies, primarily designed for military purposes. In this paper, a low-cost, effective, non-military CUAS that uses inexpensive smartphones' microphones and camera, along with machine learning models, is proposed to detect and track a malicious UAV (MUAV) in real-time. Our proposed CUAS is designed to be affordable and accessible to the general public, operating automatically to detect and track MUAVs in real-time.

关键词： Training Visualization Robot kinematics Robot sensing systems Radar tracking Cameras Autonomous aerial vehicles Real-time systems Robustness Smart phones

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