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International Conference on robotics and Artificial Intelligence (ICRai)

作者： Muhammad Haroon Asad Malik Muhammad Asim Muhammad Naeem Mumtaz Awan Muhammad Haroon Yousaf Computer Engineering Department University of Engineering and Technology Taxila Pakistan Swarm Robotics Lab National Center of Robotics and Automation University of Engineering and Technology Taxila Pakistan

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results.

关键词： Industries Visualization Semantic segmentation Public infrastructure Random access memory Transformers Autonomous aerial vehicles

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Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in robotics

arXiv

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arXiv 2025年

作者： Li, Chenhao Krause, Andreas Hutter, Marco ETH AI Center ETH Zurich Switzerland Department of Computer Science ETH Zurich Switzerland Department of Mechanical Engineering ETH Zurich Switzerland

Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimal Prescribed Performance Control for Euler-Lagrange Systems

Optimal Prescribed Performance Control for Euler-Lagrange Sy...

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International Conference on Systems and Control (ICSC)

作者： Ioanna Malli Christos Vlachos Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Greece Engineering Division and Center for AI and Robotics New York University Abu Dhabi UAE

This work introduces an optimal control policy aiming to stabilize an Euler-Lagrange system of unknown dynamics, while simultaneously satisfying predefined response criteria. The proposed methodology unfolds in two stages. In the initial phase, a neural network is trained online via an iterative process to capture the system's dynamics. Subsequently, in the second phase, by utilizing the acquired dynamics, a successive approximation algorithm is employed to derive a nearly optimal control law while taking into consideration prescribed performance specifications such as convergence speed and steady state error. Validation of our method is achieved through a simulated paradigm, confirming the success of both the identification process and the minimization of the adopted integral cost.

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A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization

A Mixing-Accelerated Primal-Dual Proximal Algorithm for Dist...

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2024 American Control Conference, ACC 2024

作者： Zichong, O. Qiu, Chenyang Wang, Dandan Jie, L. University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904-4743 United States School of Information Science and Technology Shanghaitech University Shanghai2012 China Shanghai Engineering Research Center of Energy Efficient and Custon Ai Ic Shanghai201210 China

ISBN: (纸本)9798350382655

In this paper, we develop a distributed mixing-accelerated primal-dual proximal algorithm, referred to as MAP-Pro, which enables nodes in multi-agent networks to cooperatively minimize the sum of their nonconvex, smooth local cost functions in a decentralized fashion. The proposed algorithm is constructed upon minimizing a computationally inexpensive augmented-Lagrangian-like function and incorporating a time-varying mixing polynomial to expedite information fusion across the network. The convergence results derived for MAP-Pro include a sublinear rate of convergence to a stationary solution and, under the Polyak-Lojasiewics (P-L) condition, a linear rate of convergence to the global optimal solution. Additionally, we may embed the well-noted Chebyshev acceleration scheme in MAP-Pro, which generates a specific sequence of mixing polynomials with given degrees and enhances the convergence performance based on MAP-Pro. Finally, we illustrate the competitive convergence speed and communication efficiency of MAP-Pro via a numerical example. © 2024 AACC.

关键词： Cost functions

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PACED-5G: Predictive Autonomous Control using Edge for Drones over 5G

arXiv

引用

arXiv 2023年

作者： Sankaranarayanan, Viswa Narayanan Damigos, Gerasimos Seisa, Achilleas Santi Satpute, Sumeet Gajanan Lindgren, Tore Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden Ericsson Research Luleå Sweden

With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multidimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV. © 2023, CC BY.

关键词： Drones

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Biometric Authentication in Smart Cities Using Mobile Cloud Computing

Biometric Authentication in Smart Cities Using Mobile Cloud ...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT. Near East University Nicosia TURKIYE Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management(GLBITM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare

arXiv

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arXiv 2024年

作者： Acikgoz, Emre Can Ince, Osman Batur Bench, Rayene Boz, Arda Anıl Kesen, Ilker Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center Turkey Koç University Department of Computer Engineering Turkey Hacettepe University Department of Computer Engineering Turkey Robert College Turkey Yıldız Technical University Department of Computer Engineering Turkey

The integration of Large Language Models (LLMs) into healthcare promises to transform medical diagnostics, research, and patient care. Yet, the progression of medical LLMs faces obstacles such as complex training requirements, rigorous evaluation demands, and the dominance of proprietary models that restrict academic exploration. Transparent, comprehensive access to LLM resources is essential for advancing the field, fostering reproducibility, and encouraging innovation in healthcare ai. We present Hippocrates, an open-source LLM framework specifically developed for the medical domain. In stark contrast to previous efforts, it offers unrestricted access to its training datasets, codebase, checkpoints, and evaluation protocols. This open approach is designed to stimulate collaborative research, allowing the community to build upon, refine, and rigorously evaluate medical LLMs within a transparent ecosystem. Also, we introduce Hippo, a family of 7B models tailored for the medical domain, fine-tuned from Mistral and LLaMA2 through continual pre-training, instruction tuning, and reinforcement learning from human and ai feedback. Our models outperform existing open medical LLMs models by a large-margin, even surpassing models with 70B parameters. Through Hippocrates, we aspire to unlock the full potential of LLMs not just to advance medical knowledge and patient care but also to democratize the benefits of ai research in healthcare, making them available across the globe. © 2024, CC BY.

关键词： Diagnosis

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A Resilient Framework for 5G-Edge-Connected UAVs based on Switching Edge-MPC and Onboard-PID Control

arXiv

引用

arXiv 2023年

作者： Damigos, Gerasimos Seisa, Achilleas Santi Satpute, Sumeet Gajanan Lindgren, Tore Nikolakopoulos, George Ericsson Research Luleå Sweden The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

In recent years, the need for resources for handling processes with high computational complexity for mobile robots is becoming increasingly urgent. More specifically, robots need to autonomously operate in a robust and continuous manner, while keeping high performance, a need that led to the utilization of edge computing to offload many computationally demanding and time-critical robotic procedures. However, safe mechanisms should be implemented to handle situations when it is not possible to use the offloaded procedures, such as if the communication is challenged or the edge cluster is not available. To this end, this article presents a switching strategy for safety, redundancy, and optimized behavior through an edge computing-based Model Predictive Controller (MPC) and a low-level onboard-PID controller for edge-connected Unmanned Aerial Vehicles (UAVs). The switching strategy is based on the communication Key Performance Indicators (KPIs) over 5G to decide whether the UAV should be controlled by the edge-based or have a safe fallback based on the onboard controller. © 2023, CC BY.

关键词： Three term control systems

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SVDnet: Singular Value Control and Distance Alignment Network for 3D Object Detection

引用

IEEE Transactions on Intelligent Transportation Systems 2023年第9期24卷 9281-9295页

作者： Chang, Ming-Jen Cheng, Chih-Jen Hsiao, Ching-Chun Li, Yung-Hui Huang, Ching-Chun Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan Hon Hai Research Institute AI Research Center Taipei City114699 Taiwan

The SOTA methods proposed voxelization or pillarization to regularize unordered point clouds, improving computing efficiency for LiDAR-based 3D object detection. However, they usually trade partial accuracy for speed. Thus, we bring up a new problem setting: 'Is it possible to keep high detection accuracy while point-cloud quantization is applied?'. To this end, we found that the inconsistent sparsity of the point cloud over the depth distance, which is still an open question, might be the main reason. To address the inconsistency effect, we first proposed a new pillar-based vehicle detection model, named SVDnet, in which novel plug-ins are introduced in its backbone and neck. Specifically, a novel low-rank objective is designed to force the backbone to extract distance/sparsity-aware features and suppress the other feature variations among vehicle samples. Next, we alleviated the remaining feature inconsistency resulting from distance/sparsity in the neck by dynamic feature selection and adaptive feature fusion. Here, feature selection is realized by a position attention network, while feature fusion is achieved by a Distance Alignment Ratio-generation Network (DARN). Later, the selected and fused features, less sensitive to sparsity, are concatenated and fed to an SSD-like detection head. Besides, we also integrate the proposed plug-ins with multiple pillar/voxel-based methods for performance boosting. Our evaluation shows that SVDnet improves the average precision of the distant cases by 8.11% with only 0.23 milliseconds speed drop compared with PointPillars. Furthermore, the extensional results validate that our plug-ins can help SOTA pillar/voxel-based methods to gain noticeable improvement, especially for far-range objects. © 2000-2011 IEEE.

关键词： Feature extraction

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Confidence-Aware Safe and Stable Control of Control-Affine Systems

Confidence-Aware Safe and Stable Control of Control-Affine S...

引用

American Control Conference (ACC)

作者： Shiqing Wei Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering NYU Tandon Control/Robotics Research Laboratory School of Engineering 5 Metrotech Center Brooklyn NY USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer's estimation accuracy and the fulfillment of safety and control requirements.

关键词： Accuracy Observers Control systems Eigenvalues and eigenfunctions Safety Trajectory Task analysis

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