Suchergebnisse - "deep learning in robotics and automation"

Fast and Robust Visuomotor Riemannian Flow Matching Policy

Autoren: Ding, Haoran Jaquier, Noémie Peters, Jan et al.

Quelle: IEEE Transactions on robotics. 41:5327-5343

Schlagwörter: Deep learning in robotics and automation, learning and adaptive systems, learning from demonstrations, riemannian flow matching, visuomotor policies

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-369864
https://kth.diva-portal.org/smash/get/diva2:1998239/FULLTEXT01.pdf

The voraus-AD Dataset for Anomaly Detection in Robot Applications

Autoren: Brockmann, Jan Thies Rudolph, Marco Rosenhahn, Bodo et al.

Quelle: IEEE Transactions on robotics. 40:438-451

Schlagwörter: Dataset for anomaly detection (AD), deep learning in robotics and automation, failure detection and recovery, probability and statistical models

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-201696

LeTac-MPC: Learning Model Predictive Control for Tactile-Reactive Grasping

Autoren: Zhengtong Xu Yu She

Quelle: IEEE Transactions on Robotics. 40:4376-4395

Schlagwörter: perception for grasping and manipulation, FOS: Computer and information sciences, Grasping, Computer Science - Artificial Intelligence, Grippers, Shape, tactile control, Tactile sensors, Dynamics, Computer Science - Robotics, Artificial Intelligence (cs.AI), Deep learning in robotics and automation, Industrial Engineering, Robots, Real-time systems, Robotics (cs.RO)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2403.04934

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

Autoren: Rothfuss, Jonas Ferreira, Fabio Aksoy, Eren et al.

Quelle: IEEE Robotics and Automation Letters. 3(4):4007-4014

Schlagwörter: Learning and adaptive systems, visual learning, deep learning in robotics and automation

Dateibeschreibung: electronic

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38426
https://hh.diva-portal.org/smash/get/diva2:1266021/FULLTEXT01.pdf

Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper

Autoren: Zhang, hui Peeters, jef Demeester, eric et al.

Quelle: IEEE Transactions on Robotics. 39:1244-1259

Schlagwörter: Contact modeling, Technology, 0209 industrial biotechnology, Science & Technology, 4007 Control engineering, mechatronics and robotics, grasping, reactive and sensor-based planning, Robotics, 02 engineering and technology, FORCE, deep learning in robotics and automation, MODEL, 0906 Electrical and Electronic Engineering, Industrial Engineering & Automation, 0801 Artificial Intelligence and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 0913 Mechanical Engineering

Enabling Visual Action Planning for Object Manipulation Through Latent Space Roadmap

Autoren: Martina Lippi Petra Poklukar Michael C. Welle et al.

Quelle: IEEE Transactions on Robotics

Schlagwörter: FOS: Computer and information sciences, Deep Learning in Robotics and Automation, Heuristic algorithms, Latent Space Planning, Manipulation Planning, Planning, Robots, Stacking, Task analysis, Trajectory, Visual Learning, Visualization, Computer Science - Robotics, Computer Science - Machine Learning, 0209 industrial biotechnology, 02 engineering and technology, Robotics (cs.RO), Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2103.02554
https://hdl.handle.net/11590/423028
https://doi.org/10.1109/TRO.2022.3188163
https://ieeexplore.ieee.org/document/9833914
https://doi.org/10.1109/TRO.2022.3188163
https://hdl.handle.net/11580/93946

Deep Learning Approaches to Grasp Synthesis: A Review

Autoren: Newbury, Rhys Gu, Morris Chumbley, Lachlan et al.

Quelle: IEEE Transactions on robotics. 39(5):3994-4015

Schlagwörter: deep learning in robotics and automation, Dexterous manipulation, grasping, perception for grasping and manipulation

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-349565

Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms

Autoren: Hengli Wang Rui Fan Yuxiang Sun et al.

Quelle: IEEE Transactions on Cybernetics. 52:10750-10760

Schlagwörter: FOS: Computer and information sciences, Benchmark testing, 0209 industrial biotechnology, Dynamic fusion, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Anomaly detection, 02 engineering and technology, Semantic scene understanding, Roads, Semantics, Computer Science - Robotics, Deep learning in robotics and automation, Mobile robots, 0202 electrical engineering, electronic engineering, information engineering, Feature extraction, Neural Networks, Computer, Robotics (cs.RO), Algorithms, Visualization

Zugangs-URL: http://arxiv.org/pdf/2103.02433
https://pubmed.ncbi.nlm.nih.gov/33760752
http://arxiv.org/abs/2103.02433
https://arxiv.org/pdf/2103.02433.pdf
https://ieeexplore.ieee.org/document/9385989
https://dblp.uni-trier.de/db/journals/corr/corr2103.html#abs-2103-02433
https://www.ncbi.nlm.nih.gov/pubmed/33760752
http://export.arxiv.org/pdf/2103.02433
https://research.polyu.edu.hk/en/publications/dynamic-fusion-module-evolves-drivable-area-and-road-anomaly-dete

Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning

Autoren: Nikita Rudin Hendrik Kolvenbach Vassilios Tsounis et al.

Quelle: IEEE Transactions on Robotics

Schlagwörter: FOS: Computer and information sciences, Computer Science - Robotics, 0209 industrial biotechnology, space robotics and automation, Legged Robots, Deep Learning in Robotics and Automation, 02 engineering and technology, Robotics (cs.RO)

Zugangs-URL: https://www.research-collection.ethz.ch/bitstream/20.500.11850/490128/5/Model_free_control_of_jumping_quadruped_robots_in_low_gravity.pdf
http://arxiv.org/abs/2106.09357
https://arxiv.org/pdf/2106.09357.pdf
https://arxiv.org/abs/2106.09357
http://arxiv.org/pdf/2106.09357.pdf
https://www.arxiv-vanity.com/papers/2106.09357/
https://dblp.uni-trier.de/db/journals/corr/corr2106.html#abs-2106-09357
https://arxiv.org/abs/2106.09357
https://ieeexplore.ieee.org/document/9453856

Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency

Autoren: Enrico Bellocchio Thomas A. Ciarfuglia Gabriele Costante et al.

Quelle: IEEE Robotics and Automation Letters. 4:2348-2355

Schlagwörter: Agricultural automation, computer vision for other robotic applications, deep learning in robotics and automation, robotics in agriculture and forestry, visual learning, Control and Systems Engineering, Human-Computer Interaction, Biomedical Engineering, Mechanical Engineering, Control and Optimization, Artificial Intelligence, Computer Science Applications1707 Computer Vision and Pattern Recognition, 1707, Agricultural Automation, Robotics in Agriculture and Forestry, 0202 electrical engineering, electronic engineering, information engineering, Deep Learning in Robotics and Automation, 02 engineering and technology, Computer Vision for Other Robotic Applications, Visual Learning

Dateibeschreibung: application/pdf

Zugangs-URL: https://iris.uniroma1.it/retrieve/handle/11573/1494397/1682305/Bellocchio_postprint_Weakly_2019.pdf
https://hdl.handle.net/11391/1448956
http://ieeexplore.ieee.org/servlet/opac?punumber=7083369
https://doi.org/10.1109/LRA.2019.2903260

Learning-based Localizability Estimation for Robust LiDAR Localization

Autoren: Nubert, Julian Walther, Etienne Khattak, Shehryar et al.

Quelle: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Deep Learning in Robotics and Automation, 02 engineering and technology, Machine learning (artificial intelligence), Machine Learning (cs.LG), Mobile robot localization, Computer Science - Robotics, 03 medical and health sciences, Artificial Intelligence (cs.AI), 0302 clinical medicine, Localization and Mapping, Field robotics, Robotics (cs.RO)

Zugangs-URL: http://arxiv.org/abs/2203.05698

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

Autoren: Schaupp, Lukas Bürki, Mathias Cadena, Cesar et al.

Quelle: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Schlagwörter: Localization, Deep Learning in Robotics and Automation, Range Sensing, FOS: Computer and information sciences, Computer Science - Robotics, 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Robotics (cs.RO)

Dateibeschreibung: application/application/pdf

Zugangs-URL: https://www.research-collection.ethz.ch/bitstream/20.500.11850/438727/1/1242.pdf
http://arxiv.org/abs/1903.07918
http://dblp.uni-trier.de/db/journals/corr/corr1903.html#abs-1903-07918
https://dblp.uni-trier.de/db/conf/iros/iros2019.html#SchauppBDSC19
https://www.research-collection.ethz.ch/bitstream/20.500.11850/438727/1/1242.pdf
https://www.research-collection.ethz.ch/handle/20.500.11850/438727
http://ui.adsabs.harvard.edu/abs/2019arXiv190307918S/abstract
https://arxiv.org/pdf/1903.07918.pdf
https://arxiv.org/abs/1903.07918
http://hdl.handle.net/20.500.11850/438727

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Autoren: Georgi Tinchev Adrian Penate-Sanchez Maurice Fallon et al.

Weitere Verfasser: Georgi Tinchev Adrian Penate-Sanchez Maurice Fallon et al.

Quelle: IEEE Robotics and Automation Letters, [ISSN: 2377-3766], vol. 4 (2), ( 2019)
accedaCRIS portal de investigación de la Universidad de las Palmas de Gran Canaria
instname

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, Machine Learning (cs.LG), Computer Science - Robotics, Artificial Intelligence (cs.AI), Deep learning in robotics and automation, Localization, SLAM, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Field Robots, Visual learning, 1203 Ciencia de los ordenadores, Robotics (cs.RO)

Zugangs-URL: https://ora.ox.ac.uk/objects/uuid:e181235e-f4e4-408d-8235-6899d922bf59/download_file?safe_filename=Learning%2Bto%2BSee%2Bthe%2BWood%2Bfor%2Bthe%2BTrees-%2BDeep%2BLaser%2BLocalization%2Bin%2BUrban%2Band%2BNatural%2BEnvironments%2Bon%2Ba%2BCPU.pdf&file_format=application%2Fpdf&type_of_work=Conference+item
http://arxiv.org/abs/1902.10194
http://hdl.handle.net/10553/130230
https://arxiv.org/abs/1902.10194
https://arxiv.org/pdf/1902.10194
http://doi.org/10.1109/LRA.2019.2895264
https://arxiv.org/abs/1902.10194
https://ui.adsabs.harvard.edu/abs/2019arXiv190210194T/abstract
https://dblp.uni-trier.de/db/journals/corr/corr1902.html#abs-1902-10194
https://ieeexplore.ieee.org/document/8626476/
https://ora.ox.ac.uk/objects/uuid:e181235e-f4e4-408d-8235-6899d922bf59
https://doi.org/10.1109/lra.2019.2895264

Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning

Autoren: Devo, Alessandro Mezzetti, Giacomo Costante, Gabriele et al.

Quelle: IEEE Transactions on Robotics. 36:1546-1561

Schlagwörter: 0209 industrial biotechnology, 02 engineering and technology, Target-Driven Visual Navigation, Visual-Based Navigation, Deep Learning in Robotics and Automation, Visual Learning

Zugangs-URL: https://zenodo.org/record/4785793/files/Towards%20Generalization%20in%20Target-Driven%20Visual%20Navigation%20by%20Using%20Deep%20Reinforcement%20Learning.pdf
https://research.unipg.it/handle/11391/1471943
https://ieeexplore.ieee.org/document/9102361
https://dblp.uni-trier.de/db/journals/trob/trob36.html#DevoMCFV20
https://hdl.handle.net/11391/1471943
https://doi.org/10.1109/TRO.2020.2994002

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

Autoren: Boyi Liu Lujia Wang Ming Liu et al.

Quelle: IEEE Robotics and Automation Letters. 5:3509-3516

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, Big data in robotics and automation, Computer Science - Artificial Intelligence, Computational modeling, 02 engineering and technology, Microstrip, Machine Learning (cs.LG), Computer Science - Robotics, Artificial Intelligence (cs.AI), Deep learning in robotics and automation, Motion and path planning, Robot sensing systems, Task analysis, Fuses, Cloud computing, Robotics (cs.RO)

Zugangs-URL: http://arxiv.org/pdf/1912.12204
http://arxiv.org/abs/1912.12204
http://ieeexplore.ieee.org/document/9013081/
https://dblp.uni-trier.de/db/journals/corr/corr1912.html#abs-1912-12204
https://ieeexplore.ieee.org/document/9013081/
https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002274053393025
http://dblp.uni-trier.de/db/journals/ral/ral5.html#LiuWLX20

Geometrically Mappable Image Features

Autoren: Janine Thoma Danda Pani Paudel Ajad Chhatkuli et al.

Quelle: IEEE Robotics and Automation Letters. 5:2062-2069

Schlagwörter: FOS: Computer and information sciences, Technology, Science & Technology, 4007 Control engineering, mechatronics and robotics, 4602 Artificial intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Robotics, 02 engineering and technology, PSI_VISICS, deep learning in robotics and automation, Localization, 0202 electrical engineering, electronic engineering, information engineering, PSI_4632, mapping, 0913 Mechanical Engineering

Zugangs-URL: https://lirias.kuleuven.be/bitstream/123456789/670196/2/thoma.pdf
http://arxiv.org/abs/2003.09682
http://dblp.uni-trier.de/db/journals/corr/corr2003.html#abs-2003-09682
https://ieeexplore.ieee.org/document/8977317/
https://lirias.kuleuven.be/3019641
https://dblp.uni-trier.de/db/journals/corr/corr2003.html#abs-2003-09682
https://www.research-collection.ethz.ch/handle/20.500.11850/403158
https://www.arxiv-vanity.com/papers/2003.09682/
https://ui.adsabs.harvard.edu/abs/2020arXiv200309682T/abstract
https://arxiv.org/abs/2003.09682v1
https://arxiv.org/pdf/2003.09682.pdf

Ambiguity in Sequential Data: Predicting Uncertain Futures With Recurrent Models

Autoren: Alessandro Berlati Oliver Scheel Luigi Di Stefano et al.

Quelle: IEEE Robotics and Automation Letters. 5:2935-2942

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, autonomous agents, Deep learning in robotics and automation, intelligent transportation systems, Statistics - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 02 engineering and technology, Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/2003.10381
http://arxiv.org/abs/2003.10381
https://mediatum.ub.tum.de/1630683
https://arxiv.org/abs/2003.10381
https://ieeexplore.ieee.org/document/9001185
https://doi.org/10.1109/LRA.2020.2974716
http://dblp.uni-trier.de/db/journals/corr/corr2003.html#abs-2003-10381
https://cris.unibo.it/handle/11585/806525
http://export.arxiv.org/pdf/2003.10381
http://arxiv.org/pdf/2003.10381.pdf
https://arxiv.org/abs/2003.10381
https://ui.adsabs.harvard.edu/abs/2020arXiv200310381B/abstract
https://ieeexplore.ieee.org/document/9001185
https://doi.org/10.1109/LRA.2020.2974716
https://hdl.handle.net/11585/806525

Learning Natural Locomotion Behaviors for Humanoid Robots Using Human Bias

Autoren: Chuanyu Yang Kai Yuan Shuai Heng et al.

Quelle: Yang, C, Yuan, K, Heng, S, Komura, T & Li, Z 2020, ' Learning natural locomotion behaviors for humanoid robots using human bias ', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2610-2617 . https://doi.org/10.1109/LRA.2020.2972879

Schlagwörter: FOS: Computer and information sciences, Computer Science - Robotics, 0209 industrial biotechnology, Deep learning in robotics and automation, humanoid and bipedal locomotion, learning from demonstration, 02 engineering and technology, Robotics (cs.RO)

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.pure.ed.ac.uk/ws/files/144085854/Learning_natural_locomotion_YANG_DOA22012020_AFV.pdf
http://arxiv.org/abs/2005.10195
https://www.research.ed.ac.uk/en/publications/learning-natural-locomotion-behaviors-for-humanoid-robots-using-h
https://ieeexplore.ieee.org/document/8990011
https://www.research.ed.ac.uk/portal/en/publications/learning-natural-locomotion-behaviors-for-humanoid-robots-using-human-bias(886eb6b9-39d0-418b-987e-93676da8ed9d).html
http://dblp.uni-trier.de/db/journals/ral/ral5.html#YangYHKL20
https://dblp.uni-trier.de/db/journals/ral/ral5.html#YangYHKL20
https://www.pure.ed.ac.uk/ws/files/144085854/Learning_natural_locomotion_YANG_DOA22012020_AFV.pdf
https://arxiv.org/abs/2005.10195
https://dblp.uni-trier.de/db/journals/corr/corr2005.html#abs-2005-10195
http://arxiv.org/pdf/2005.10195.pdf
https://ui.adsabs.harvard.edu/abs/2020arXiv200510195Y/abstract
https://hdl.handle.net/20.500.11820/886eb6b9-39d0-418b-987e-93676da8ed9d
https://www.pure.ed.ac.uk/ws/files/144085854/Learning_natural_locomotion_YANG_DOA22012020_AFV.pdf

Deep Reinforcement Learning for Instruction Following Visual Navigation in 3D Maze-Like Environments

Autoren: Alessandro Devo Gabriele Costante Paolo Valigi

Quelle: IEEE Robotics and Automation Letters. 5:1175-1182

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Deep learning in robotics and automation, visual learning, visual-based navigation, 01 natural sciences, 0105 earth and related environmental sciences

Zugangs-URL: https://ieeexplore.ieee.org/document/8957297
https://research.unipg.it/handle/11391/1458050
http://dblp.uni-trier.de/db/journals/ral/ral5.html#DevoCV20
https://dblp.uni-trier.de/db/journals/ral/ral5.html#DevoCV20
https://doi.org/10.1109/LRA.2020.2965857
https://hdl.handle.net/11391/1458050
http://ieeexplore.ieee.org/servlet/opac?punumber=7083369
https://doi.org/10.1109/LRA.2020.2965857

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Autoren: Julian Nubert Johannes Kohler Vincent Berenz et al.

Quelle: IEEE Robotics and Automation Letters, 5 (2)

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, 0209 industrial biotechnology, optimization and optimal control, motion control, Deep learning in robotics and automation, redundant robots, robust/adaptive control of robotic systems, Systems and Control (eess.SY), 02 engineering and technology, Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), Computer Science - Robotics, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Robotics (cs.RO)

Dateibeschreibung: application/application/pdf

Zugangs-URL: http://arxiv.org/pdf/1912.10360
http://arxiv.org/abs/1912.10360
https://arxiv.org/abs/1912.10360
https://arxiv.org/pdf/1912.10360.pdf
https://ui.adsabs.harvard.edu/abs/2019arXiv191210360N/abstract
https://ieeexplore.ieee.org/abstract/document/9006856
https://dblp.uni-trier.de/db/journals/corr/corr1912.html#abs-1912-10360
https://publications.rwth-aachen.de/record/795966
https://doi.org/10.1109/LRA.2020.2975727
http://dblp.uni-trier.de/db/journals/ral/ral5.html#NubertKBAT20
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3311035
http://hdl.handle.net/20.500.11850/406451