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arXiv 2021年

作者： Wagener, Nolan Boots, Byron Cheng, Ching-An Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Microsoft Research RedmondWA United States

Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the development of safe reinforcement learning (RL) algorithms that produce a safe policy after training, ensuring safety during training as well remains an open problem. A fundamental challenge is performing exploration while still satisfying constraints in an unknown Markov decision process (MDP). In this work, we address this problem for the chance-constrained setting. We propose a new algorithm, SAILR, that uses an intervention mechanism based on advantage functions to keep the agent safe throughout training and optimizes the agent’s policy using off-the-shelf RL algorithms designed for unconstrained MDPs. Our method comes with strong guarantees on safety during both training and deployment (i.e., after training and without the intervention mechanism) and policy performance compared to the optimal safety-constrained policy. In our experiments, we show that SAILR violates constraints far less during training than standard safe RL and constrained MDP approaches and converges to a well-performing policy that can be deployed safely without intervention. Our code is available at https://***/nolanwagener/safe_rl. © 2021, CC BY.

关键词： Markov processes

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Cryo-CACTI: Cryogenic-Aware CACTI for Cache Modeling down to 10K in Advanced 7nm FinFETs

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IEEE Transactions on computers 2025年

作者： Ravipati, Divya Praneetha van Santen, Victor M. Parihar, Shivendra Singh Chauhan, Yogesh Singh Panda, Preeti Ranjan Amrouch, Hussam Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Technical University of Munich TUM School of Computation Information and Technology Chair of AI Processor Design Munich Institute of Robotics and Machine Intelligence Germany Kanpur Nanolab Department of Electrical Engineering Uttar Pradesh India

Cryogenic circuits are currently employed in fields such as quantum computing, particle detectors, magnetic resonance imaging, and space applications. While cryogenic circuits are being researched, there is limited work on designing cryogenic caches at temperatures below 77K. Moreover, there is no tool to estimate the delay, power, and area of cryogenic caches at advanced technology nodes. Our research focuses on the development of cryogenic caches tailored for the 7nm technology node, operating at 10K. However, a key challenge is the lack of cryogenic measurement data, especially in recent technologies. Consequently, through conducting our own FinFET transistor measurements, we calibrate cryogenic transistor models at 10K. With the 7nm cryogenic transistor data, we model Cryo-CACTI for cryogenic caches (due to cache’s vital role in improving performance and their considerable share in area and power of the processor). Using Cryo-CACTI, our evaluation reveals considerable improvements in the energy efficiency (up to 99%) of cryogenic caches of larger sizes compared to the caches at room temperature (300K). Additionally, we explore alternative cache configurations at circuit-level to optimize cryogenic operation. Furthermore, we use Cryo-CACTI to explore the performance/energy consumption of cryogenic caches while simulating workloads such as SPEC CPU2017 and machine learning via neural networks. © 1968-2012 IEEE.

关键词： Combinatorial circuits

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Optimal Sampling for Shape from Focus by Using Gaussian Process Regression

Optimal Sampling for Shape from Focus by Using Gaussian Proc...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Hoon-Seok Jang Guhnoo Yun Muhammad Tariq Mahmood Min-Koo Kang Center for Imaging Media Research Korea Institute of Science and Technology Korea Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Korea School of Computer Science and Engineering Korea University of Technology and Education Korea

ISBN: (数字)9781728151861

ISBN: (纸本)9781728151878

Shape from Focus (SFF) is one of passive optical methods for estimating 3D shape of an object. In SFF, a large number of 2D images with different focus levels are required. The number of images may affect the complexity and the accuracy of the results. In this manuscript, a Gaussian process regression (GPR) method is proposed to get 3D shape from the minimum number of 2D images. The proposed method (***) is applied to fit focus curves, which are obtained by applying one of focus measure operators. Experimental results demonstrate the effectiveness of the proposed method.

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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International Conference on robotics and Automation Engineering (ICRAE)

作者： Li Chengbo Wang Xiaoming Huang Zixi Pang Guangyao Han Xinyu Lin Yaguang Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Middle School Shenzhen China School of Data Science and Software Engineering Wuzhou University Wuzhou China Advanced Robotics and Intelligent Control Centre Singapore Polytechnic Singapore

ISBN: (纸本)9781665406987

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the “Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm”. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot.

关键词： Automation Heuristic algorithms Earthquakes Mathematical models Scheduling Optimization Rescue robots

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On Session Continuation among Slices for Inter-Slice Mobility Support in 3GPP Service-based Architecture

On Session Continuation among Slices for Inter-Slice Mobilit...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Muhammad Mohtasim Sajjad Dhammika Jayalath Yu-Chu Tian Carlos J. Bernardos School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Computer Science Queensland University of Technology Brisbane Australia Universidad Carlos III de Madrid Spain

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

The 3GPP has provided its first standard specifications for network slicing in the recent Release 15. The fundamental principles are specified which constitute the standard network slicing framework. These specifications, however, lack the session continuation mechanisms among slices, which is a fundamental requirement to achieve inter-slice mobility. In this paper, we propose three solutions which enable session continuation among slices in the current 3GPP network slicing framework. These solutions are based on existing, well-established standard mechanisms. The first solution is based on the Return Routability/Binding Update (RR/BU) procedure of the popular Internet standard, Mobile IPv6 (MIPv6). The second solution is based on the 3GPP standard GPRS Tunnelling Protocol User Plane (GTPv1-U), which establishes a GTP tunnel between previous and new slice for session continuation. The third solution is a hybrid solution of both MIPv6-RR/BU and GTPv1-U protocols. We compare the performance of all these solutions through analytical modelling. Results show that the GTPv1-U based and the hybrid MIPv6/GTPv1-U solutions promise lower service disruption latency, however, incur higher resource utilization overhead compared to MIPv6-RR/BU and 3GPP standard PDU Session Establishment process.

关键词： 3GPP 5G mobile communication Network slicing Tunneling Protocols IP networks

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Internet of Mobile Energy: Towards seamless energy trading across the transport and energy sectors.

arXiv

引用

arXiv 2020年

作者： Jurdak, Raja Dorri, Ali Vilathgamuwa, Mahinda School of Computer Science Queensland University of Technology Brisbane Australia School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia CSIRO Data61 Brisbane Australia

The rapid growth in distributed energy sources on power grids is leading to more decentralised energy management systems for the prediction of power supply and demand and the dynamic setting of an energy price signal. Within this emerging smart grid, electric vehicles can serve as consumers, transporters, and providers of energy through two-way charging stations, which highlights the importance of predicting vehicle mobility and battery state of health to the effectiveness of decentralised energy management. This paper proposes a vision for an Internet of Mobile Energy (IoME), where energy and information flow seamlessly across the power and transport sectors to enhance the grid stability and end user welfare. Energy predictions that drive the price signals are used to guide electric vehicle mobility decisions, which in turn underpin the prediction of mobility patterns and future energy predictions. We propose an information architecture for IoME that incorporates data observations across all participants, a blockchain framework for data transmissions, and machine learning for energy and mobility predictions. We present an example scenario that details the seamless and closed loop information flow across the energy and transport sectors, along with a blockchain design and transaction vocabulary for trusted decentralised transactions. We finally discuss the open challenges presented by IoME that can unlock signifcant benefits to grid stability, innovation, and end user welfare. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Core challenges of social robot navigation: A survey

arXiv

引用

arXiv 2021年

作者： Mavrogiannis, Christoforos Baldini, Francesca Wang, Allan Zhao, Dapeng Trautman, Pete Steinfeld, Aaron Oh, Jean Paul G. Allen School of Computer Science & Engineering University of Washington United States Honda Research Institute California Institute of Technology United States Robotics Institute Carnegie Mellon University United States Honda Research Institute United States

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade. Copyright © 2021, The Authors. All rights reserved.

关键词： Human robot interaction

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Synchronous Motion Generation of Multiple Continuum Robots Based on a Jacobian-Estimation Strategy

Synchronous Motion Generation of Multiple Continuum Robots B...

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IEEE International Conference on robotics and Biomimetics

作者： Ning Tan Ruikun Hu Yuyang Wu Xinyu Zhang Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education (East China Normal University) Shanghai PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665405362

In this paper, a novel model-free control scheme is proposed for synchronous motion control of multiple continuum robots. The control problem is formulated as two convex optimization routines in an adaptive and model-free closed-loop framework. The proposed approach is heading to address cooperative task execution for multiple continuum robots. The model-free feedback control guarantees the high portability for a wide range of continuum robots. Simulation studies are conducted on two kinds of tasks, which are cooperative task execution with two robots and simultaneously tracking control with three robots. Besides, the superiority of proposed approach is validated through comparisons with other methods. Simulation results prove the high accuracy and efficiency of this model-free feedback control scheme.

关键词： Jacobian matrices Adaptation models Tracking Simulation Conferences Convex functions Feedback control

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Aeroengine remaining useful life prediction via integrating enhanced inverted transformer and spatiotemporal graph learning

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ISA Transactions 2025年

作者： Sun, Shilong Ding, Hao Zhao, Zida Zhou, Yu Wang, Dong Xu, Wenfu School of Robotics and Advanced Manufacturing College of Artificial Intelligence Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai200240 China

Accurate prediction of aeroengine Remaining Useful Life (RUL) is critical for ensuring flight safety, minimizing maintenance costs, and improving operational efficiency. This study proposes a novel model, the Fourier-Enhanced Inverted Transformer with Graph-Augmented Spatiotemporal Modeling (FIT-GSTM), to enhance RUL prediction performance. FIT-GSTM combines an inverted Transformer with a Spatiotemporal Graph Convolutional Network (STGCN) to effectively capture global spatiotemporal dependencies across multi-sensor data. To further enrich feature representation, the model incorporates Fast Fourier Transform (FFT) to extract frequency-domain information and fuses it with time-domain features, enhancing robustness to noise. Additionally, the integration of Memory Tokens and Reversible Instance Normalization (RevIN) strengthens the model's ability to retain long-term dependencies and adapt to heterogeneous data distributions. Experimental evaluations on the C-MAPSS dataset demonstrate that FIT-GSTM achieves superior RUL prediction accuracy and generalization compared to existing methods, highlighting its potential for real-world deployment in aeroengine health management. © 2025 ISA

关键词： Fast Fourier transforms

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