| Year |
Citation |
Score |
| 2022 |
Stabinger S, Peer D, Piater J, Rodríguez-Sánchez A. Evaluating the progress of deep learning for visual relational concepts. Journal of Vision. 21: 8. PMID 34636844 DOI: 10.1167/jov.21.11.8 |
0.335 |
|
| 2020 |
Hangl S, Dunjko V, Briegel HJ, Piater J. Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition. Frontiers in Robotics and Ai. 7: 42. PMID 33501210 DOI: 10.3389/frobt.2020.00042 |
0.423 |
|
| 2019 |
Zech P, Renaudo E, Haller S, Zhang X, Piater J. Action representations in robotics: A taxonomy and systematic classification The International Journal of Robotics Research. 38: 518-562. DOI: 10.1177/0278364919835020 |
0.425 |
|
| 2019 |
Krivic S, Piater JH. Pushing corridors for delivering unknown objects with a mobile robot Autonomous Robots. 43: 1435-1452. DOI: 10.1007/S10514-018-9804-8 |
0.478 |
|
| 2019 |
Lakani SR, Rodríguez-Sánchez AJ, Piater JH. Towards affordance detection for robot manipulation using affordance for parts and parts for affordance Autonomous Robots. 43: 1155-1172. DOI: 10.1007/S10514-018-9787-5 |
0.435 |
|
| 2018 |
Shukla D, Erkent Ö, Piater JH. Learning Semantics of Gestural Instructions for Human-Robot Collaboration. Frontiers in Neurorobotics. 12: 7. PMID 29615888 DOI: 10.3389/Fnbot.2018.00007 |
0.497 |
|
| 2018 |
Savarimuthu TR, Buch AG, Schlette C, Wantia N, Robmann J, Martinez D, Alenya G, Torras C, Ude A, Nemec B, Kramberger A, Worgotter F, Aksoy EE, Papon J, Haller S, ... Piater J, et al. Teaching a Robot the Semantics of Assembly Tasks Ieee Transactions On Systems, Man, and Cybernetics. 48: 670-692. DOI: 10.1109/Tsmc.2016.2635479 |
0.434 |
|
| 2018 |
Jamone L, Ugur E, Cangelosi A, Fadiga L, Bernardino A, Piater J, Santos-Victor J. Affordances in Psychology, Neuroscience, and Robotics: A Survey Ieee Transactions On Cognitive and Developmental Systems. 10: 4-25. DOI: 10.1109/Tcds.2016.2594134 |
0.442 |
|
| 2018 |
Wächter M, Ovchinnikova E, Wittenbeck V, Kaiser P, Szedmák S, Mustafa W, Kraft D, Krüger N, Piater JH, Asfour T. Integrating multi-purpose natural language understanding, robot’s memory, and symbolic planning for task execution in humanoid robots Robotics and Autonomous Systems. 99: 148-165. DOI: 10.1016/J.Robot.2017.10.012 |
0.449 |
|
| 2017 |
Zech P, Haller S, Lakani SR, Ridge B, Ugur E, Piater JH. Computational models of affordance in robotics: a taxonomy and systematic classification: Adaptive Behavior. 25: 235-271. DOI: 10.1177/1059712317726357 |
0.393 |
|
| 2017 |
Ugur E, Piater J. Emergent Structuring of Interdependent Affordance Learning Tasks Using Intrinsic Motivation and Empirical Feature Selection Ieee Transactions On Cognitive and Developmental Systems. 9: 328-340. DOI: 10.1109/Tcds.2016.2581307 |
0.459 |
|
| 2017 |
Hangl S, Ugur E, Piater JH. Autonomous robots: potential, advances and future direction Elektrotechnik Und Informationstechnik. 134: 293-298. DOI: 10.1007/S00502-017-0516-0 |
0.465 |
|
| 2016 |
Stabinger S, Rodríguez-Sánchez A, Piater J. Learning Abstract Classes using Deep Learning Arxiv: Computer Vision and Pattern Recognition. 524-528. DOI: 10.4108/Eai.3-12-2015.2262468 |
0.438 |
|
| 2016 |
Xiong H, Szedmak S, Piater J. Learning undirected graphical models using persistent sequential Monte Carlo Machine Learning. 103: 239-260. DOI: 10.1007/S10994-016-5564-X |
0.432 |
|
| 2016 |
Hoyoux T, Rodríguez-Sánchez AJ, Piater JH. Can computer vision problems benefit from structured hierarchical classification? Machine Vision and Applications. 1-14. DOI: 10.1007/s00138-016-0763-9 |
0.31 |
|
| 2015 |
Xiong H, Rodríguez-Sánchez AJ, Szedmak S, Piater J. Diversity priors for learning early visual features. Frontiers in Computational Neuroscience. 9: 104. PMID 26321941 DOI: 10.3389/Fncom.2015.00104 |
0.437 |
|
| 2015 |
Xiong H, Rodríguez-Sánchez AJ, Szedmak S, Piater J. Diversity priors for learning early visual features Frontiers in Computational Neuroscience. 9. DOI: 10.3389/fncom.2015.00104 |
0.364 |
|
| 2015 |
Worgotter F, Geib C, Tamosiunaite M, Aksoy EE, Piater J, Xiong H, Ude A, Nemec B, Kraft D, Kruger N, Wachter M, Asfour T. Structural bootstrapping-A novel, generative mechanism for faster and more efficient acquisition of action-knowledge Ieee Transactions On Autonomous Mental Development. 7: 140-154. DOI: 10.1109/Tamd.2015.2427233 |
0.492 |
|
| 2015 |
Ugur E, Piater J. Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning Proceedings - Ieee International Conference On Robotics and Automation. 2015: 2627-2633. DOI: 10.1109/ICRA.2015.7139553 |
0.429 |
|
| 2015 |
Hangl S, Ugur E, Szedmak S, Piater J, Ude A. Reactive, task-specific object manipulation by metric reinforcement learning Proceedings of the 17th International Conference On Advanced Robotics, Icar 2015. 557-564. DOI: 10.1109/ICAR.2015.7251511 |
0.435 |
|
| 2015 |
Ugur E, Piater J. Refining discovered symbols with multi-step interaction experience Ieee-Ras International Conference On Humanoid Robots. 2015: 1007-1012. DOI: 10.1109/HUMANOIDS.2015.7363477 |
0.453 |
|
| 2015 |
Xiong H, Szedmák S, Piater JH. Scalable, accurate image annotation with joint SVMs and output kernels Neurocomputing. 169: 205-214. DOI: 10.1016/J.Neucom.2014.11.096 |
0.43 |
|
| 2015 |
Rodríguez-Sánchez AJ, Neumann H, Piater JH. Beyond Simple and Complex Neurons: Towards Intermediate-level Representations of Shapes and Objects KüNstliche Intelligenz. 29: 19-29. DOI: 10.1007/S13218-014-0341-0 |
0.306 |
|
| 2015 |
Hofbaur M, Müller A, Piater J, Rinner B, Steinbauer G, Vincze M, Wögerer C. Making Better Robots—Austria’s contribution to the European Robotics Research Roadmap | Making Better Robots – Beiträge Österreichs zur Europäischen Robotics Research Roadmap Elektrotechnik Und Informationstechnik. 132: 237-248. DOI: 10.1007/S00502-015-0304-7 |
0.335 |
|
| 2014 |
Ugur E, Szedmak S, Piater J. Complex affordance learning based on basic affordances 2014 22nd Signal Processing and Communications Applications Conference, Siu 2014 - Proceedings. 698-701. DOI: 10.1109/SIU.2014.6830325 |
0.439 |
|
| 2014 |
Szedmak S, Ugur E, Piater J. Knowledge propagation and relation learning for predicting action effects Ieee International Conference On Intelligent Robots and Systems. 623-629. DOI: 10.1109/IROS.2014.6942624 |
0.362 |
|
| 2014 |
Ugur E, Piater J. Emergent structuring of interdependent affordance learning tasks Ieee Icdl-Epirob 2014 - 4th Joint Ieee International Conference On Development and Learning and On Epigenetic Robotics. 489-494. DOI: 10.1109/DEVLRN.2014.6983028 |
0.46 |
|
| 2014 |
Ugur E, Szedmak S, Piater J. Bootstrapping paired-object affordance learning with learned single-affordance features Ieee Icdl-Epirob 2014 - 4th Joint Ieee International Conference On Development and Learning and On Epigenetic Robotics. 476-481. DOI: 10.1109/DEVLRN.2014.6983026 |
0.476 |
|
| 2014 |
Teney D, Piater JH. Multiview feature distributions for object detection and continuous pose estimation Computer Vision and Image Understanding. 125: 265-282. DOI: 10.1016/J.Cviu.2014.04.012 |
0.394 |
|
| 2013 |
Krüger N, Janssen P, Kalkan S, Lappe M, Leonardis A, Piater J, Rodríguez-Sánchez AJ, Wiskott L. Deep hierarchies in the primate visual cortex: what can we learn for computer vision? Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 1847-71. PMID 23787340 DOI: 10.1109/Tpami.2012.272 |
0.41 |
|
| 2013 |
Wörgötter F, Aksoy EE, Krüger N, Piater J, Ude A, Tamosiunaite M. A simple ontology of manipulation actions based on hand-object relations Ieee Transactions On Autonomous Mental Development. 5: 117-134. DOI: 10.1109/Tamd.2012.2232291 |
0.432 |
|
| 2011 |
Detry R, Kraft D, Kroemer O, Bodenhagen L, Peters J, Krüger N, Piater JH. Learning Grasp Affordance Densities Paladyn: Journal of Behavioral Robotics. 2: 1-17. DOI: 10.2478/S13230-011-0012-X |
0.509 |
|
| 2011 |
Piater J, Jodogne S, Detry R, Kraft D, Krüger N, Kroemer O, Peters J. Learning visual representations for perception-action systems The International Journal of Robotics Research. 30: 294-307. DOI: 10.1177/0278364910382464 |
0.525 |
|
| 2011 |
Krüger N, Geib C, Piater J, Petrick R, Steedman M, Wörgötter F, Ude A, Asfour T, Kraft D, Omrčen D, Agostini A, Dillmann R. ObjectAction Complexes: Grounded abstractions of sensorymotor processes Robotics and Autonomous Systems. 59: 740-757. DOI: 10.1016/J.Robot.2011.05.009 |
0.444 |
|
| 2010 |
Kraft D, Detry R, Pugeault N, Başeski E, Guerin F, Piater JH, Krüger N. Development of object and grasping knowledge by robot exploration Ieee Transactions On Autonomous Mental Development. 2: 368-383. DOI: 10.1109/Tamd.2010.2069098 |
0.449 |
|
| 2010 |
Erkan AN, Kroemer O, Detry R, Altun Y, Piater J, Peters J. Learning probabilistic discriminative models of grasp affordances under limited supervision Ieee/Rsj 2010 International Conference On Intelligent Robots and Systems, Iros 2010 - Conference Proceedings. 1586-1591. DOI: 10.1109/IROS.2010.5650088 |
0.418 |
|
| 2010 |
Kroemer OB, Detry R, Piater J, Peters J. Combining active learning and reactive control for robot grasping Robotics and Autonomous Systems. 58: 1105-1116. DOI: 10.1016/J.Robot.2010.06.001 |
0.524 |
|
| 2010 |
Baeski E, Pugeault N, Kalkan S, Bodenhagen L, Piater JH, Krüger N. Using multi-modal 3D contours and their relations for vision and robotics Journal of Visual Communication and Image Representation. 21: 850-864. DOI: 10.1016/J.Jvcir.2010.06.006 |
0.449 |
|
| 2009 |
Detry R, Pugeault N, Piater JH. A probabilistic framework for 3D visual object representation. Ieee Transactions On Pattern Analysis and Machine Intelligence. 31: 1790-803. PMID 19696450 DOI: 10.1109/Tpami.2009.64 |
0.418 |
|
| 2007 |
Jodogne S, Piater JH. Closed-loop learning of visual control policies Journal of Artificial Intelligence Research. 28: 349-391. DOI: 10.1613/Jair.2110 |
0.456 |
|
| 2007 |
Declercq A, Piater JH. On-line simultaneous learning and tracking of visual feature graphs Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2007.383435 |
0.37 |
|
| 2007 |
Scalzo F, Piater JH. Adaptive patch features for object class recognition with learned hierarchical models Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2007.383371 |
0.378 |
|
| 2006 |
Scalzo F, Piater JH. Unsupervised learning of dense hierarchical appearance representations Proceedings - International Conference On Pattern Recognition. 2: 395-398. DOI: 10.1109/ICPR.2006.1144 |
0.381 |
|
| 2006 |
Jodogne S, Briquet C, Piater JH. Approximate policy iteration for closed-loop learning of visual tasks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4212: 210-221. |
0.391 |
|
| 2006 |
Jodogne S, Piater JH. Task-driven discretization of the joint space of visual percepts and continuous actions Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4212: 222-233. |
0.331 |
|
| 2005 |
Jodogne S, Piater JH. Interactive learning of mappings from visual percepts to actions Icml 2005 - Proceedings of the 22nd International Conference On Machine Learning. 393-400. DOI: 10.1145/1102351.1102401 |
0.367 |
|
| 2001 |
Coelho J, Piater J, Grupen R. Developing haptic and visual perceptual categories for reaching and grasping with a humanoid robot Robotics and Autonomous Systems. 37: 195-218. DOI: 10.1016/S0921-8890(01)00158-0 |
0.663 |
|
| 2000 |
Piater JH, Grupen RA. Distinctive features should be learned Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1811: 52-61. DOI: 10.1007/3-540-45482-9_6 |
0.659 |
|
| 2000 |
Piater JH, Grupen RA. Feature learning for recognition with Bayesian networks Proceedings - International Conference On Pattern Recognition. 15: 17-29. |
0.584 |
|
| 1999 |
Piater JH, Grupen RA. Learning real-time stereo vergence control Ieee International Symposium On Intelligent Control - Proceedings. 272-277. |
0.632 |
|
| 1999 |
Piater JH, Grupen RA. Toward learning visual discrimination strategies Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 1: 410-415. |
0.652 |
|
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