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2020 Index IEEE Robotics and Automation Letters Vol. 5

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g32gcto57vduveq2eb7bes5y7a" style="color: black;">IEEE Robotics and Automation Letters</a> </i> &nbsp;
Park, J.S., +, LRA April 2020 1024-1031 Real Time Trajectory Prediction Using Deep Conditional Generative Models.  ...  ., +, LRA Oct. 2020 5417-5424 Deep Learning Based Real-Time OCT Image Segmentation and Correction for Robotic Needle Insertion Systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lra.2020.3032821">doi:10.1109/lra.2020.3032821</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qrnouccm7jb47ipq6w3erf3cja">fatcat:qrnouccm7jb47ipq6w3erf3cja</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201122042758/https://ieeexplore.ieee.org/ielx7/7083369/9133350/09260113.pdf?tp=&amp;arnumber=9260113&amp;isnumber=9133350&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lra.2020.3032821"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Table of Contents

<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g32gcto57vduveq2eb7bes5y7a" style="color: black;">IEEE Robotics and Automation Letters</a> </i> &nbsp;
Hipp, and M. Reichert 2357 Recognition and Prediction of Surgical Actions Based on Online Robotic Tool Detection . . . . . . . . J. Park and C. H.  ...  : Active Perception of Target Objects Using Multimodal Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lra.2021.3072707">doi:10.1109/lra.2021.3072707</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qyphyzqxfrgg7dxdol4qamrdqu">fatcat:qyphyzqxfrgg7dxdol4qamrdqu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210423020442/https://ieeexplore.ieee.org/ielx7/7083369/9285111/09410476.pdf?tp=&amp;arnumber=9410476&amp;isnumber=9285111&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/71/1e/711ed71aabd9bd0f86a8f42d61d8e109fecfe98d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/lra.2021.3072707"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Using Visual Anomaly Detection for Task Execution Monitoring [article]

Santosh Thoduka and Juergen Gall and Paul G. Plöger
<span title="2021-07-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A probabilistic U-Net architecture is used to learn to predict optical flow, and the robot's kinematics and 3D model are used to model camera and body motion.  ...  Our method learns to predict the motions that occur during the nominal execution of a task, including camera and robot body motion.  ...  They use force, sound and kinematic signals from a service robot to learn from nominal executions using hidden Markov models (HMMs) and Gaussian processes. Inceoglu et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.14206v1">arXiv:2107.14206v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cyncui2gjjexfka2rl7uzgehqe">fatcat:cyncui2gjjexfka2rl7uzgehqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210805014358/https://arxiv.org/pdf/2107.14206v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2b/62/2b6232cf41d060fa958faf01f6285dc7c87b2aa8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.14206v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey of Traversability Estimation for Mobile Robots [article]

Christos Sevastopoulos, Stations Konstantopoulos
<span title="2022-04-22">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Finally, we discuss how self-supervised learning can help satisfy deep methods' increased need for (challenging to acquire and label) large-scale datasets.  ...  up to the recent proliferation of deep learning literature.  ...  An end-to-end deep reinforcement learning approach for a mobile robot navigating an unknown environment is portrayed by [77] in which the inputs are raw depth images and the control commands serve as  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.10883v1">arXiv:2204.10883v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gg2zvyyzpveknadszbnpjaojme">fatcat:gg2zvyyzpveknadszbnpjaojme</a> </span>
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From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots

Mark Pfeiffer, Michael Schaeuble, Juan Nieto, Roland Siegwart, Cesar Cadena
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nytnrt3gtzbixld5r6a4talbky" style="color: black;">2017 IEEE International Conference on Robotics and Automation (ICRA)</a> </i> &nbsp;
In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot.  ...  To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform.  ...  Regarding mobile robotic applications of end-to-end learning, Ross et al. [14] presented an approach that learns a left/right controller for an unmanned aerial vehicle (UAV) based on image data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icra.2017.7989182">doi:10.1109/icra.2017.7989182</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icra/PfeifferSNSC17.html">dblp:conf/icra/PfeifferSNSC17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wduhbrjy55e3xcbttq3fkyi5ta">fatcat:wduhbrjy55e3xcbttq3fkyi5ta</a> </span>
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2019 Index IEEE Transactions on Industrial Informatics Vol. 15

<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lxfdtuyn4nhhtevm3datmysrpa" style="color: black;">IEEE Transactions on Industrial Informatics</a> </i> &nbsp;
Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches.  ...  Park, S., +, TII Feb. 2019 998-1006 Robotic Arm Based Automatic Ultrasound Scanning for Three-Dimensional Imaging.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tii.2020.2968165">doi:10.1109/tii.2020.2968165</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/utk3ywxc6zgbdbfsys5f4otv7u">fatcat:utk3ywxc6zgbdbfsys5f4otv7u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107081018/https://ieeexplore.ieee.org/ielx7/9424/8924788/09031596.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tii.2020.2968165"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

AI based Algorithms of Path Planning, Navigation and Control for Mobile Ground Robots and UAVs [article]

Jian Zhang
<span title="2021-10-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This report investigates path planning and control strategies for mobile robots with machine learning techniques, including ground mobile robots and flying UAVs.  ...  As the demands of autonomous mobile robots are increasing in recent years, the requirement of the path planning/navigation algorithm should not be content with the ability to reach the target without any  ...  For supervised learning, the learning process is based on pre-labeled data input.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.00910v1">arXiv:2110.00910v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ch7hjsz5wzbidn5geplu3w5ha4">fatcat:ch7hjsz5wzbidn5geplu3w5ha4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211010024243/https://arxiv.org/ftp/arxiv/papers/2110/2110.00910.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/94/8594812b60c024c415e686911a748ea6d49f6237.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.00910v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [article]

Jing Zhang, Dacheng Tao
<span title="2020-11-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing.  ...  In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity  ...  Recently, deep reinforcement learning has been used for autonomous robot control, e.g., self-driving vehicle [203] , [199] , medical robot [213] , mobile service robot.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.08612v1">arXiv:2011.08612v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dflut2wdrjb4xojll34c7daol4">fatcat:dflut2wdrjb4xojll34c7daol4</a> </span>
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Table of contents

<span title="">2010</span> <i title="IEEE"> ICCAS 2010 </i> &nbsp;
method using stereo vision for container auto-landing system 872 HeeJoo Yoon, YoungChul Hwang and EuiYoung Cha TEP-40 TEP-42 Design of Embeded Image Data Processing System 887 Yuxia LI and  ...  in Process Control I TP09-1 Model Predictive Control based on ARX models 448 Junichiro Kon and Yoshiyuki Yamashita TP09-2 Dynamic tuning of PI-controllers based on model-free reinforcement learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccas.2010.5669694">doi:10.1109/iccas.2010.5669694</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/43uj6owxh5c63gr2mn5shli5ja">fatcat:43uj6owxh5c63gr2mn5shli5ja</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200919111638/https://ieeexplore.ieee.org/ielx5/5656240/5669634/05669694.pdf?tp=&amp;arnumber=5669694&amp;isnumber=5669634&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iccas.2010.5669694"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

VR content creation and exploration with deep learning: A survey

Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang
<span title="2020-03-23">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jnfwhcgai5dalfpeugj6pkswji" style="color: black;">Computational Visual Media</a> </i> &nbsp;
This article surveys recent research that uses such deep learning methods for VR content creation and exploration.  ...  VR content creation and exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely  ...  using mobile phones.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41095-020-0162-z">doi:10.1007/s41095-020-0162-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lgogzx26bvhn5f7uyefjkz7zny">fatcat:lgogzx26bvhn5f7uyefjkz7zny</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200325184141/https://link.springer.com/content/pdf/10.1007/s41095-020-0162-z.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ac/63/ac634eae6c732eba0ec027ad71f1fed47a8cdbec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41095-020-0162-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [article]

Zhaoxin Fan, Yazhi Zhu, Yulin He, Qi Sun, Hongyan Liu, Jun He
<span title="2022-04-21">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, survey study about the latest development of deep learning-based methods is lacking.  ...  Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others.  ...  The progress of deep learning-based object pose detection and tracking is inseparable from the use and construction of many large-scale datasets that are both challenging and credible.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.14291v2">arXiv:2105.14291v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2kxd4owthvf7tbcbnlqlqu4r3m">fatcat:2kxd4owthvf7tbcbnlqlqu4r3m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220425102934/https://arxiv.org/pdf/2105.14291v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/3f/ce3f4b25bfb125f7d4adc6d370898ffba0440c97.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.14291v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Visual Affordance and Function Understanding: A Survey [article]

Mohammed Hassanin, Salman Khan, Murat Tahtali
<span title="2018-07-18">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature.  ...  Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment.  ...  Visual affordance is a branch of research that deals with the affordances as computer vision problem based on images and videos; and uses the machine learning methods, such as deep learning, to solve these  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.06775v1">arXiv:1807.06775v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kl7miygnizdinpovqnzfdhwokq">fatcat:kl7miygnizdinpovqnzfdhwokq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929153152/https://arxiv.org/pdf/1807.06775v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/81/d3/81d327ec41c67728b15438bca86d10b72de1d88f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1807.06775v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Graph Neural Networks in IoT: A Survey [article]

Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E. Barnes, Mehdi Boukhechba
<span title="2022-03-31">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data.  ...  Continuous sensing generates massive amounts of data and presents challenges for machine learning.  ...  Nowadays, deep learning based methods provide a set of feasible solutions to robotics and autonomous vehicle planning and control problems [121] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.15935v2">arXiv:2203.15935v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkqg5ukg5fezbewu5mr5hqsp4e">fatcat:jkqg5ukg5fezbewu5mr5hqsp4e</a> </span>
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A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles

Usman Gidado Manzo, Haruna Chiroma, Nahla Aljojo, Saidu Abubakar, Segun I. Popoola, Mohammed Ali Al-Garadi
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
DEEP LEARNING FRAMEWORK AND LIBRARY Numerous ML frameworks and libraries offer the possibility of utilizing GPU accelerators to speed up the learning process with supported interfaces; some allow the use  ...  TAXONOMY OF DEEP LEARNING ARCHITECTURES FOR STEERING ANGLE PREDICTION OF AUTONOMOUS VEHICLES Taxonomy enables researchers to identify classifications used in a specific research area based on the most  ...  The literature review has covered the following DL architectures in steering control: deep neural network, convolutional neural network, hybrid DL and deep reinforcement learning.  ... 
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Conference Guide [Front matter]

<span title="2020-12-13">2020</span> <i title="IEEE"> 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) </i> &nbsp;
Furthermore, the developed framework is proven capable of transferring the learning into different environments and mobility platforms using a small amount of training data.  ...  16:40-17:00 SuCT6.3 Multi-Sensor-Based Predictive Control for Autonomous Parking in Presence of Pedestrians (I), pp. 406-413.  ...  modifies the values in the communication links to interfere in the consensus process.  ... 
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