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检索条件"主题词=deep learning in robotics and automation"
221 条 记 录,以下是111-120 订阅
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Hysteresis Compensator With learning-Based Hybrid Joint Angle Estimation for Flexible Surgery Robots
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IEEE robotics AND automation LETTERS 2020年 第4期5卷 6837-6844页
作者: Baek, Donghoon Seo, Ju-Hwan Kim, Joonhwan Kwon, Dong-Soo Korea Adv Inst Sci & Technol Robot Program Daejeon South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon South Korea EasyEndo Surg Inc Seoul South Korea
Hysteresis causes difficulties in precisely controlling motion of flexible surgery robots and degrades the surgical performance. In order to reduce hysteresis, model-based feed-forward and feedback-based methods using... 详细信息
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Real-Time Soft Body 3D Proprioception via deep Vision-Based Sensing
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3382-3389页
作者: Wang, Ruoyu Wang, Shiheng Du, Songyu Xiao, Erdong Yuan, Wenzhen Feng, Chen NYU Tandon Sch Engn Brooklyn NY 11201 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA
Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to... 详细信息
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learning Natural Locomotion Behaviors for Humanoid Robots Using Human Bias
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2610-2617页
作者: Yang, Chuanyu Yuan, Kai Heng, Shuai Komura, Taku Li, Zhibin Univ Edinburgh Edinburgh EH8 9JS Midlothian Scotland Harbin Inst Technol Harbin 150001 Peoples R China
This letter presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and rob... 详细信息
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A General Framework for Uncertainty Estimation in deep learning
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3153-3160页
作者: Loquercio, Antonio Segu, Mattia Scaramuzza, Davide Univ Zurich Dept Informat & Neuroinformat CH-8092 Zurich Switzerland Swiss Fed Inst Technol CH-8092 Zurich Switzerland
Neural networks predictions are unreliable when the input sample is out of the training distribution or corrupted by noise. Being able to detect such failures automatically is fundamental to integrate deep learning al... 详细信息
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Self-Supervised learning of State Estimation for Manipulating Deformable Linear Objects
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2372-2379页
作者: Yan, Mengyuan Zhu, Yilin Jin, Ning Bohg, Jeannette Stanford Univ Sch Engn Stanford CA 94305 USA Stanford Univ Stanford CA 94305 USA Calico Labs San Francisco CA USA
We demonstrate model-based, visual robot manipulation of deformable linear objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple... 详细信息
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When Your Robot Breaks: Active learning During Plant Failure
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 438-445页
作者: Schrum, Mariah L. Gombolay, Matthew C. Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA
Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals. To address this challenging problem, we propose probabilis... 详细信息
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SilhoNet-Fisheye: Adaptation of A ROI Based Object Pose Estimation Network to Monocular Fisheye Images
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IEEE robotics AND automation LETTERS 2020年 第3期5卷 4241-4248页
作者: Billings, Gideon Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA
There has been much recent interest in deep learning methods for monocular image based object pose estimation. While object pose estimation is an important problem for autonomous robot interaction with the physical wo... 详细信息
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Data-Efficient and Safe learning for Humanoid Locomotion Aided by a Dynamic Balancing Model
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IEEE robotics AND automation LETTERS 2020年 第3期5卷 4376-4383页
作者: Ahn, Junhyeok Lee, Jaemin Sentis, Luis Univ Texas Austin Dept Mech Engn Austin TX 78712 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA
In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relie... 详细信息
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Fast Underwater Image Enhancement for Improved Visual Perception
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3227-3234页
作者: Islam, Md Jahidul Xia, Youya Sattar, Junaed Univ Minnesota Dept Comp Sci & Engn Interact Robot & Vis Lab Minneapolis MN 55455 USA
In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates ... 详细信息
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Federated Imitation learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3509-3516页
作者: Liu, Boyi Wang, Lujia Liu, Ming Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Cloud Comp Lab Shenzhen 518000 Peoples R China Univ Chinese Acad Sci Shenzhen 518000 Peoples R China Hong Kong Univ Sci & Technol Dept ECE Hong Kong Peoples R China Univ Macau Macau 999078 Peoples R China
Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the ne... 详细信息
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