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共 88 条记录，以下是71-80 订阅

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Stealthy Adversaries against Uncertain Cyber-Physical Systems: Threat of Robust Zero-Dynamics Attack

Stealthy Adversaries against Uncertain Cyber-Physical System...

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作者： Park, Gyunghoon Lee, Chanhwa Shim, Hyungbo Eun, Yongsoon Johansson, Karl H. Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul136-791 Korea Republic of Research and Development Division Hyundai Motor Company Gyeonggi-do18280 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Information and Communication Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu47988 Korea Republic of ACCESS Linnaeus Centre School of Electrical Engineering KTH Royal Institute of Technology Stockholm144 28 Sweden

In this paper, we address the problem of constructing a robust stealthy attack that compromises uncertain cyber-physical systems having unstable zeros. We first interpret the (non-robust) conventional zero-dynamics attack based on Byrnes-Isidori normal form, and then present a new robust zero-dynamics attack for uncertain plants. Different from the conventional strategy, our key idea is to isolate the real zero-dynamics from the plant's input-output relation and to replace it with an auxiliary nominal zero-dynamics. As a result, this alternative attack does not require the exact model knowledge anymore. The price to pay for the robustness is to utilize the input and output signals of the system (i.e., disclosure resources). It is shown that a disturbance observer can be employed to realize the new attack philosophy when there is a lack of model knowledge. Simulation results with a hydro-turbine power system are presented to verify the attack performance and robustness. © 1963-2012 IEEE.

关键词： Cyber Physical System

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Learning contact locations for pushing and orienting unknown objects

Learning contact locations for pushing and orienting unknown...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tucker Hermans Fuxin Li James M. Rehg Aaron F. Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Center for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA

We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes the outcome trajectories of the manipulations and computes either a push-stability or rotate-push score for each trial. The robot then learns a regression function for each score in order to predict push effectiveness as a function of object shape. With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.

关键词： Robots Shape Trajectory Feature extraction Histograms Kernel Training

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Decoupling behavior, perception, and control for autonomous learning of affordances

Decoupling behavior, perception, and control for autonomous ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tucker Hermans James M. Rehg Aaron F. Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467356404

A novel behavior representation is introduced that permits a robot to systematically explore the best methods by which to successfully execute an affordance-based behavior for a particular object. The approach decomposes affordance-based behaviors into three components. We first define controllers that specify how to achieve a desired change in object state through changes in the agent's state. For each controller we develop at least one behavior primitive that determines how the controller outputs translate to specific movements of the agent. Additionally we provide multiple perceptual proxies that define the representation of the object that is to be computed as input to the controller during execution. A variety of proxies may be selected for a given controller and a given proxy may provide input for more than one controller. When developing an appropriate affordance-based behavior strategy for a given object, the robot can systematically vary these elements as well as note the impact of additional task variables such as location in the workspace. We demonstrate the approach using a PR2 robot that explores different combinations of controller, behavior primitive, and proxy to perform a push or pull positioning behavior on a selection of household objects, learning which methods best work for each object.

关键词： Grippers End effectors Planning Adaptive control Vectors Feedback control

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The motion grammar: Linguistic perception, planning, and control

The motion grammar: Linguistic perception, planning, and con...

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International Conference on robotics Science and Systems, RSS 2011

作者： Dantam, Neil Stilman, Mike School of Interactive Computing Center for Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Dr. NW AtlantaGA30332 United States

ISBN: (纸本)9780262517799

We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ability to guarantee completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained gameplay for the roughly six minute duration of each match.

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Finding graph topologies for feasible multirobot motion planning

Finding graph topologies for feasible multirobot motion plan...

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2012 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Pushkar Kolhe Henrik I. Christensen Center for Robotics and Intelligent Machines School of Interactive Computing at the Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467317375

In this paper we present a design methodology for mapping the configuration space of multirobot systems in a warehouse. The method generates a tree topology with robot and terminal nodes. In the most general case multirobot motion planning problems are NP-complete. But, we show that pebble motion problems in the resultant tree topology are always feasible. Our method uses an integer programming formulation to find such a solution.

关键词： Robots Topology Junctions Planning Linear programming Steiner trees Network topology

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Guided Pushing for Object Singulation

Guided Pushing for Object Singulation

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IEEE/RSJ International Conference on intelligent robotics and Systems

作者： Tucker Hermans James M. Rehg Aaron Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA.

ISBN: (纸本)9781467317375

We propose a novel method for a robot to separate and segment objects in a cluttered tabletop environment. The method leverages the fact that external object boundaries produce visible edges within an object cluster. We achieve this singulation of objects by using the robot arm to perform pushing actions specifically selected to test whether particular visible edges correspond to object boundaries. We verify the separation of objects after a push by examining the clusters formed by geometric segmentation of regions residing on the table surface. To avoid explicitly representing and tracking edges across push behaviors we aggregate over all edges in a given orientation by representing the push-history as an orientation histogram. By tracking the history of directions pushed for each object cluster we can build evidence that a cluster cannot be further separated. We present quantitative and qualitative experimental results performed in a real home environment by a mobile manipulator using input from an RGB-D camera mounted on the robot's head. We show that our pushing strategy can more reliably obtain singulation in fewer pushes than an approach, that does not explicitly reason about boundary information.

关键词： reason pushes environment

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The Motion Grammar Calculus for Context-Free Hybrid Systems

The Motion Grammar Calculus for Context-Free Hybrid Systems

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American Control Conference

作者： Neil Dantam Mike Stilman Robotics and Intelligent Machines Center in the Department of Interactive Computing Georgia Institute of Technology Atlanta GA 30332 USA

ISBN: (纸本)9781457710957

This paper provides a method for deriving provably correct controllers for Hybrid Dynamical Systems with Context-Free discrete dynamics, nonlinear continuous dynamics, and nonlinear state partitioning. The proposed method models the system using a Context-Free Motion Grammar and specifies correct performance using a Regular language representation such as Linear Temporal Logic. The initial model is progressively rewritten via a calculus of symbolic transformation rules until it satisfies the desired specification.

关键词： hybrid systems context-free Calculus syntax Calculi

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Enhancing the robot service experience through social media

Enhancing the robot service experience through social media

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20th IEEE International Symposium on Robot and Human interactive Communication, RO-MAN 2011

作者： Emeli, Victor Christensen, Henrik Center for Robotics and Intelligent Machines School of Interactive Computing Georgia Institute of Technology Atlanta GA United States

ISBN: (纸本)9781457715716

The development of effective robot servers has been an intensely studied research area for many years. Their operation in a dynamic environment presents multiple challenges in navigation, recognition, human-robot interaction, and manipulation. This paper addresses the challenges presented by recognition and human-robot interaction by utilizing Twitter and Facebook to potentially mitigate some of the long known issues in these areas. Specifically, we will address the problems of reliable human-robot communications and efficiency of service delivery. An online scenario study is administered to participants to evaluate the performance of a social media enabled robot as compared to a traditional speech synthesis and recognition enabled robot. The potential for the robot to learn and build long-term relationships through access to these social mediums is also discussed as a future work. © 2011 IEEE.

关键词： Human robot interaction

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Push planning for object placement on cluttered table surfaces

Push planning for object placement on cluttered table surfac...

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2011 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Akansel Cosgun Tucker Hermans Victor Emeli Mike Stilman Center of Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA USA

We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation.

关键词： Planning Clutter Robots Search problems Shape Grasping Heuristic algorithms

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