检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Bentley, Michael Rucker, Caleb Kuntz, Alan Robotics Center School of Computing University of Utah Salt Lake CityUT84112 United States University of Tennessee KnoxvilleTN37996 United States

Tendon-driven robots, where one or more tendons under tension bend and manipulate a flexible backbone, can improve minimally invasive surgeries involving difficult-to-reach regions in the human body. Planning motions safely within constrained anatomical environments requires accuracy and efficiency in shape estimation and collision checking. Tendon robots that employ arbitrarily-routed tendons can achieve complex and interesting shapes, enabling them to travel to difficult-to-reach anatomical regions. Arbitrarily-routed tendon-driven robots have unintuitive nonlinear kinematics. Therefore, we envision clinicians leveraging an assistive interactive-rate motion planner to automatically generate collision-free trajectories to clinician-specified destinations during minimally-invasive surgical procedures. Standard motion-planning techniques cannot achieve interactive-rate motion planning with the current expensive tendon robot kinematic models. In this work, we present a 3-phase motion-planning system for arbitrarily-routed tendon-driven robots with a Precompute phase, a Load phase, and a Supervisory Control phase. Our system achieves an interactive rate by developing a fast kinematic model (over 1,000 times faster than current models), a fast voxel collision method (27.6 times faster than standard methods), and leveraging a precomputed roadmap of the entire robot workspace with pre-voxelized vertices and edges. In simulated experiments, we show that our motion-planning method achieves high tip-position accuracy and generates plans at 14.8 Hz on average in a segmented collapsed lung pleural space anatomical environment. Our results show that our method is 17,700 times faster than popular off-the-shelf motion planning algorithms with standard FK and collision detection approaches. Our open-source code is available online. © 2022, CC BY.

关键词： Motion planning

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

IEEE International Conference on robotics and Biomimetics

作者： Zhihao Li Junjia Liu Zhuo Li Minghao Yu Tao Teng Shunbo Zhou Miao Li Tin Lun Lam Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong SAR Hong Kong Center for Logistics Robotics Hong Kong SAR 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: (数字)9798331509644

ISBN: (纸本)9798331509651

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.

关键词： Translation Large language models Scalability Humanoid robots Detectors Search problems Generators Planning Complexity theory Mobile robots

来源：评论

学校读者我要写书评

暂无评论

View-Robust Neural Networks for Unseen Human Action Recognition in Videos

View-Robust Neural Networks for Unseen Human Action Recognit...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yu, Jiahui Ma, Tianyu Ju, Zhaojie Chen, Hang Xu, Yingke Zhejiang University Department of Biomedical Engineering Hangzhou China Binjiang Institute of Zhejiang University Innovation Center for Smart Medical Technologies & Devices Hangzhou China Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Data-driven deep learning achieved excellent performance for human action recognition. However, unseen action recognition remains a challenge for most existing neural networks. Because the action categories, collection perspectives, and scenarios considered during data collection are limited. Compared with class-unseen action recognition, view-unseen action recognition in videos is under-explored. This paper proposes view-robust neural networks (VR-Net) to recognize unseen actions in videos. The VR-Net consists of a 3D pose estimation module, skeleton adaptive transformation neural networks, and classification modules. We first extract 3D skeleton models from the video sequence based on existing pose estimation methods. Next, we propose a skeleton representation transformation scheme and achieve it based on Convolutional Neural Networks (VR-CNN) and Graph Neural Networks (VR-GCN), resulting in the optimal skeleton representations. Futhermore, we explore an associate optimization scheme and a fused output method. We evaluate the proposed neural networks on three challenging benchmarks, i.e., NTU RGB-D dataset (NTU), Kinetics-400 dataset, and Human3.6M dataset (H3.6M). The experimental results show that view robust neural networks achieve the top performance compared to state-of-the-art RGB-based and skeleton-based works, such as 93.6% on the NTU (CV) and 94.6% on the Kinetics-400 dataset (Top-5). The proposed neural networks significantly improve the recognition performance for unseen action recognition, such as 86.8% on the H3.6M (View 2). © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Open-Pose 3D zero-shot learning: Benchmark and challenges

引用

Neural Networks 2025年 181卷 106775页

作者： Zhao, Weiguang Yang, Guanyu Zhang, Rui Jiang, Chenru Yang, Chaolong Yan, Yuyao Hussain, Amir Huang, Kaizhu Department of Computer Science University of Liverpool LiverpoolL69 7ZX United Kingdom Department of Foundational Mathematics Xi'an Jiaotong-Liverpool University Suzhou215123 China Department of Mechatronics and Robotics Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Robotics Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Computing Edinburgh Napier University EdinburghEH11 4BN United Kingdom Data Science Research Center Duke Kunshan University Kunshan215316 China

With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CLIP) to 3D vision have made significant progress in the 3D zero-shot classification task. These methods primarily focus on 3D object classification with an aligned pose;such a setting is, however, rather restrictive, which overlooks the recognition of 3D objects with open poses typically encountered in real-world scenarios, such as an overturned chair or a lying teddy bear. To this end, we propose a more realistic and challenging scenario named open-pose 3D zero-shot classification, focusing on the recognition of 3D objects regardless of their orientation. First, we revisit the current research on 3D zero-shot classification and propose two benchmark datasets specifically designed for the open-pose setting. We empirically validate many of the most popular methods in the proposed open-pose benchmark. Our investigations reveal that most current 3D zero-shot classification models suffer from poor performance, indicating a substantial exploration room towards the new direction. Furthermore, we study a concise pipeline with an iterative angle refinement mechanism that automatically optimizes one ideal angle to classify these open-pose 3D objects. In particular, to make validation more compelling and not just limited to existing CLIP-based methods, we also pioneer the exploration of knowledge transfer based on Diffusion models. While the proposed solutions can serve as a new benchmark for open-pose 3D zero-shot classification, we discuss the complexities and challenges of this scenario that remain for further research development. The code is available publicly at https://***/weiguangzhao/Diff-OP3D © 2024

关键词： Zero-shot 3D classification Open-pose Text–image matching

来源：评论

学校读者我要写书评

暂无评论

GVD-Exploration: An Efficient Autonomous Robot Exploration Framework Based on Fast Generalized Voronoi Diagram Extraction

arXiv

引用

arXiv 2023年

作者： Chen, Dingfeng Xiao, Anxing Zou, Meiyuan Chi, Wenzheng Wang, Jiankun Sun, Lining The Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China The School of Computing National University of Singapore Singapore117417 Singapore Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Rapidly-exploring Random Trees (RRTs) are a popular technique for autonomous exploration of mobile robots. However, the random sampling used by RRTs can result in inefficient and inaccurate frontiers extraction, which affects the exploration performance. To address the issues of slow path planning and high path cost, we propose a framework that uses a generalized Voronoi diagram (GVD) based multi-choice strategy for robot exploration. Our framework consists of three components: a novel mapping model that uses an end-to-end neural network to construct GVDs of the environments in real time;a GVD-based heuristic scheme that accelerates frontiers extraction and reduces frontiers redundancy;and a multi-choice frontiers assignment scheme that considers different types of frontiers and enables the robot to make rational decisions during the exploration process. We evaluate our method on simulation and real-world experiments and show that it outperforms RRT-based exploration methods in terms of efficiency and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Planning Sensing Sequences for Subsurface 3D Tumor Mapping

Planning Sensing Sequences for Subsurface 3D Tumor Mapping

引用

International Symposium on Medical robotics (ISMR)

作者： Brian Y. Cho Tucker Hermans Alan Kuntz Robotics Center and School of Computing University of Utah Salt Lake City UT USA NVIDIA

ISBN: (纸本)9781665406239

Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able to sense and map subsurface anatomy, such as tumors, efficiently and accurately. In this work, we present a method that plans a sequence of sensing actions to map the 3D geometry of subsurface tumors. We leverage a sequential Bayesian Hilbert map to create a 3D probabilistic occupancy model that represents the likelihood that any given point in the anatomy is occupied by a tumor, conditioned on sensor readings. We iteratively update the map, utilizing Bayesian optimization to determine sensing poses that explore unsensed regions of anatomy and exploit the knowledge gained by previous sensing actions. We demonstrate our method’s efficiency and accuracy in three anatomical scenarios including a liver tumor scenario generated from a real patient’s CT scan. The results show that our proposed method significantly outperforms comparison methods in terms of efficiency while detecting subsurface tumors with high accuracy.

关键词： Solid modeling Three-dimensional displays Surgery Liver Robot sensing systems Sensors Bayes methods

来源：评论

学校读者我要写书评

暂无评论

Toward Learning Context-Dependent Tasks from Demonstration for Tendon-Driven Surgical Robots

Toward Learning Context-Dependent Tasks from Demonstration f...

引用

International Symposium on Medical robotics (ISMR)

作者： Yixuan Huang Michael Bentley Tucker Hermans Alan Kuntz Robotics Center and School of Computing University of Utah Salt Lake City UT USA NVIDIA

ISBN: (纸本)9781665406239

Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future, the automation of surgical tasks for these robots may help reduce surgeon strain in the face of a rapidly growing population. However, directly encoding surgical tasks and their associated context for these robots is infeasible. In this work we take steps toward a system that is able to learn to successfully perform context-dependent surgical tasks by learning directly from a set of expert demonstrations. We present three models trained on the demonstrations conditioned on a vector encoding the context of the demonstration. We then use these models to plan and execute motions for the tendon-driven robot similar to the demonstrations for novel context not seen in the training set. We demonstrate the efficacy of our method on three surgery-inspired tasks.

关键词： Training Medical robotics Sociology Surgery Encoding Task analysis Statistics

来源：评论

学校读者我要写书评

暂无评论

Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers

arXiv

引用

arXiv 2023年

作者： Huang, Yixuan Taylor, Nichols Crawford Conkey, Adam Liu, Weiyu Hermans, Tucker University of Utah Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT United States HRL Laboratories MalibuCA United States Georgia Tech GA United States NVIDIA Corporation United States

Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they manipulate will interact with structural elements of the environment for all but the simplest of tasks. As such, we’d like our robots to reason about how multiple objects and environmental elements relate to one another and how those relations may change as the robot interacts with the world. We examine the problem of predicting inter-object and object-environment relations between previously unseen objects and novel environments purely from partial-view point clouds. Our approach enables robots to plan and execute sequences to complete multi-object manipulation tasks defined from logical relations. This removes the burden of providing explicit, continuous object states as goals to the robot. We explore several different neural network architectures for this task. We find the best performing model to be a novel transformer-based neural network that both predicts object-environment relations and learns a latent-space dynamics function. We achieve reliable sim-to-real transfer without any fine-tuning. Our experiments show that our model understands how changes in observed environmental geometry relate to semantic relations between objects. We show more videos on our website: https://***/view/erelationaldynamics. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Personalized Content Synthesis with Diffusion Model

arXiv

引用

arXiv 2024年

作者： Zhang, Xulu Wei, Xiaoyong Hu, Wentao Wu, Jinlin Wu, Jiaxin Zhang, Zhaoxiang Lei, Zhen Li, Qing Department of Computing The Hong Kong Polytechnic University 999077 Hong Kong Center for Artificial Intelligence and Robotics HKISI CAS 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing100000 China School of Artificial Intelligence UCAS Beijing100000 China

Recent advancements in diffusion models have significantly impacted content creation, leading to the emergence of Personalized Content Synthesis (PCS). By utilizing a small set of user-provided examples featuring the same subject, PCS aims to tailor this subject to specific user-defined prompts. Over the past two years, more than 150 methods have been introduced in this area. However, existing surveys primarily focus on text-to-image generation, with few providing up-to-date summaries on PCS. This paper provides a comprehensive survey of PCS, introducing the general frameworks of PCS research, which can be categorized into optimization-based and learning-based approaches. We analyze the strengths, limitations, and key techniques of these methodologies. Additionally, we explore specialized tasks within the field, such as personalized object generation, face synthesis, and style personalization, while highlighting their unique challenges and innovations. Despite the promising progress, we also discuss ongoing challenges, including overfitting and the trade-off between subject fidelity and text alignment. Through this detailed overview and analysis, we propose future directions to further the development of PCS. © 2024, CC BY.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

Understanding the Impact of Image Quality and Distance of Ob...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yu Hao Haoyang Pei Yixuan Lyu Zhongzheng Yuan John-Ross Rizzo Yao Wang Yi Fang NYU Multimedia and Visual Computing Lab NYU Tandon and NYU Abu Dhabi NYU Tandon School of Engineering New York University USA NYU Langone Health USA NYUAD Center for Artificial Intelligence and Robotics NYU Abu Dhabi UAE

Object detection is a fundamental task for autonomous driving, which aim to identify and localize objects within an image. Deep learning has made great strides for object detection, with popular models including Faster R-CNN, YOLO, and SSD. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Furthermore, original images are often compressed and uploaded to a remote server for object detection. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. As existing models are optimized for uncompressed (or lightly compressed) images over a narrow range of spatial resolution, we develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the ‘tolerated’ image size (constrained by the available bandwidth or storage). We further assess the impact of the distance of an o

关键词：

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：