检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Karimi, Zohre Ho, Shing-Hei Thach, Bao Kuntz, Alan Brown, Daniel S. Robotics Center The Kahlert School of Computing The University of Utah Salt Lake CityUT84112 United States

Automating robotic surgery via learning from demonstration (LfD) techniques is extremely challenging. This is because surgical tasks often involve sequential decision-making processes with complex interactions of physical objects and have low tolerance for mistakes. Prior works assume that all demonstrations are fully observable and optimal, which might not be practical in the real world. This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations. The method then learns a policy by optimizing the learned reward function using reinforcement learning (RL). We show that using a learned reward function to obtain a policy is more robust than pure imitation learning. We apply our approach on a physical surgical electrocautery task and demonstrate that our method can perform well even when the provided demonstrations are suboptimal and the observations are high-dimensional point clouds. Code and videos available here: https://***/view/lfdinelectrocautery Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic surgery

来源：评论

学校读者我要写书评

暂无评论

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks

arXiv

引用

arXiv 2024年

作者： Thompson, Jordan Cho, Brian Y. Brown, Daniel S. Kuntz, Alan The Robotics Center the Kahlert School of Computing The University of Utah Salt Lake CityUT84112 United States

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot’s operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate one use of this model through a trajectory optimization method that explicitly reasons about the workspace uncertainty to minimize the probability of collision. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

GSON: A Group-based Social Navigation Framework with Large Multimodal Model

arXiv

引用

arXiv 2024年

作者： Luo, Shangyi Zhu, Ji Sun, Peng Deng, Yuhong Yu, Cunjun Xiao, Anxing Wang, Xueqian The Center for Artificial Intelligence and Robotics Tsinghua Shenzhen International Graduate School Shenzhen China School of Computing National University of Singapore Singapore

As the number of service robots and autonomous vehicles in human-centered environments grows, their requirements go beyond simply navigating to a destination. They must also take into account dynamic social contexts and ensure respect and comfort for others in shared spaces, which poses significant challenges for perception and planning. In this paper, we present a group-based social navigation framework (GSON) to enable mobile robots to perceive and exploit the social group of their surroundings by leveling the visual reasoning capability of the Large Multimodal Model (LMM). For perception, we apply visual prompting techniques to zero-shot extract the social relationship among pedestrians and combine the result with a robust pedestrian detection and tracking pipeline to alleviate the problem of low inference speed of the LMM. Given the perception result, the planning system is designed to avoid disrupting the current social structure. We adopt a social structure-based mid-level planner as a bridge between global path planning and local motion planning to preserve the global context and reactive response. The proposed method is validated on real-world mobile robot navigation tasks involving complex social structure understanding and reasoning. Experimental results demonstrate the effectiveness of the system in these scenarios compared with several baselines. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Leveraging Graph Neural Networks in Transferring Multimodal Knowledge for Unimodal Segmentation

Leveraging Graph Neural Networks in Transferring Multimodal ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Tianyi Liu Jiongshu Wang Haochuan Jiang Kaizhu Huang School of Robotics Xi’ an Jiaotong-Liverpool University School of Computing and Mathematical Sciences University of Leicester Data Science Research Center Duke Kunshan University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate segmentation of brain tumors in MRI scans is crucial for effective treatment. Multimodal MRI, including FLAIR, Tlce, T1, and T2 images, provides valuable infor-mation for tumor delineation. However, challenges such data corruption and varying protocols can lead to missing modal-ities, affecting segmentation accuracy. Recent methods at-tempt to transfer knowledge from multimodal teacher mod-els to unimodal student models, but often struggle to capture complex structural relationships within the data. This paper introduces a novel approach called Graph Neural Network-based Knowledge Distillation (GKD), which uses Graph Neu-ral Networks to create a comprehensive graph connecting in-termediate layers of both teacher and student networks. This enables the student network to better learn essential features and their interactions. Evaluations on the BraTS2018 and BraTS2020 datasets demonstrate that GKD significantly en-hances brain tumor segmentation accuracy by deepening the connection between teacher and student insights. The code is publicly available at https://***/T-Y-Liu/GKD.

关键词： Knowledge engineering Image segmentation Accuracy Protocols Codes Magnetic resonance imaging Brain tumors Graph neural networks Data models Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Instruction-Following Long-Horizon Manipulation by LLM-Empowered Symbolic Planner

Instruction-Following Long-Horizon Manipulation by LLM-Empow...

引用

2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Li, Zhihao Liu, Junjia Li, Zhuo Yu, Minghao Teng, Tao Zhou, Shunbo Li, Miao Lam, Tin Lun Chen, Fei T-Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Computing Technologies Co. Ltd. Shenzhen China Institute of Technological Sciences Wuhan University Wuhan China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665481090

One of the grand goals in robotics is to develop a versatile robot capable of executing a wide array of tasks based on language instructions. These tasks often involve long-horizon manipulation due to the complexity of the real-world tasks and the high-dimensional action space of the dual-arm mobile robots. This work proposes a generalizable framework that combines a Large Language Model, a Visual-language Model, and symbolic planning to address long-horizon bimanual mobile manipulation tasks. We use the Large Language Model as the commonsense reasoner to break down the task into a set of subgoals and generate goal-oriented state queries. Subsequently, we fine-tune a Visual-language Model to obtain answers to queries. Finally, we propose a Planning Domain Definition Language translator that translates the subgoal and the goal-oriented states into a symbolic planning problem that can be solved by search-based planners. The experiments show that the proposed method has generalization ability in novel objects and instruction. It outperforms the baseline methods across all the configurations. Moreover, our approach exhibits competence in increasingly complex tasks, encompassing those that demand upward of 10 planning steps. © 2024 IEEE.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Uncertainty Aware Forward and Inverse Kinematics for Tendon-Driven Continuum Robots via Mixture Density Networks

引用

Journal of Medical robotics Research 2025年第1-2期10卷

作者： Thompson, Jordan Cho, Brian Y. Brown, Daniel S. Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake City 84112 UT United States

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate uses of this model through both a trajectory optimization method that explicitly reasons about the workspace uncertainty to minimize the probability of collision and an inverse kinematics method that maximizes the likelihood of occupying a desired geometry. © 2025 World Scientific Publishing Company.

关键词： Continuum robots kinematics medical robotics

来源：评论

学校读者我要写书评

暂无评论

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model

引用

Biomedical Engineering Frontiers 2022年第1期3卷 298-308页

作者： Heqin Zhu Qingsong Yao Li Xiao S.Kevin Zhou Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China Center for Medical Imaging RoboticsAnalytic Computing&Learning(MIRACLE)School of Biomedical Engineering&Suzhou Institute for Advanced ResearchUniversity of Science and Technology of ChinaSuzhou 215123China

Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark *** accurate and automatic localization of anatomical landmarks plays an essential role in medical image ***,recent deep learning-based methods only utilize limited data from a single *** is promising and desirable to build a model learned from different regions which harnesses the power of big *** model consists of a local network and a global network,which capture local features and global features,*** local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global *** evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical *** experimental results show that our model improves the detection accuracy compared to the state-of-the-art *** model makes the first attempt to train a single network on multiple datasets for landmark *** results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.

关键词： convolution utilize separable

来源：评论

学校读者我要写书评

暂无评论

The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

引用

Computers, Materials & Continua 2024年第9期80卷 4513-4542页

作者： Muhammad Dawood Shanshan Tu Chuangbai Xiao Muhammad Haris Hisham Alasmary Muhammad Waqas Sadaqat Ur Rehman Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user *** significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security ***,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity machine learning

来源：评论

学校读者我要写书评

暂无评论

DefGoalNet: Contextual Goal Learning from Demonstrations for Deformable Object Manipulation

DefGoalNet: Contextual Goal Learning from Demonstrations for...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Bao Thach Tanner Watts Shing-Hei Ho Tucker Hermans Alan Kuntz Robotics Center and Kahlert School of Computing University of Utah Salt Lake City UT USA NVIDIA Corporation Seattle WA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method’s effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical real-world applications.

关键词： Knowledge engineering Heart Shape Neural networks Pipelines Trajectory Task analysis

来源：评论

学校读者我要写书评

暂无评论

Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery

arXiv

引用

arXiv 2023年

作者： Cho, Brian Y. Kuntz, Alan The Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States

Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, A∗ search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：