Mårten Björkman

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Danica KragicProfessor of Computer Science, KTH - Royal Institute of TechnologyDirección de correo verificada de kth.se
Ali GhadirzadehEmbark StudiosDirección de correo verificada de embark-studios.com
Jan-Olof EklundhProfessor of Computer Science, KTH Royal Institute of Technology, StockholmDirección de correo verificada de csc.kth.se
Wenjie YinKTH Royal Institute of TechnologyDirección de correo verificada de kth.se
Atsuto MakiProfessor of Computer Science, KTH Royal Institute of TechnologyDirección de correo verificada de kth.se
Patric JensfeltKTH Royal Institute of TechnologyDirección de correo verificada de kth.se
Christian SmithAssociate Professor, Division of Robotics, Perception, and Learning, KTHDirección de correo verificada de kth.se
Niklas BergströmUniversity of TokyoDirección de correo verificada de ipc.i.u-tokyo.ac.jp
Yasemin BekiroğluChalmers University of Technology, University College London (UCL)Dirección de correo verificada de chalmers.se
Virgile HögmanPhD student, KTH Royal Institute of Technology, StockholmDirección de correo verificada de kth.se
Jeannette BohgAssistant Professor, Stanford UniversityDirección de correo verificada de stanford.edu
Judith BütepageSenior Research Engineer, SEED @ EADirección de correo verificada de kth.se
John FolkessonAssociate Professor of Robotics, KTH SwedenDirección de correo verificada de kth.se
Matthew Johnson-RobersonProfessor of Robotics, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Henrik Iskov ChristensenDirector / Professor @ UCSDDirección de correo verificada de ucsd.edu
Carl Henrik EkUniversity of CambridgeDirección de correo verificada de cam.ac.uk
Per StenströmChalmers University of TechnologyDirección de correo verificada de chalmers.se
Alessandro PieropanCTO @ Univrses ABDirección de correo verificada de kth.se
Gert KootstraWageningen University & ResearchDirección de correo verificada de wur.nl
Petter ÖgrenProfessor in Computer Science and Mobile Systems, KTH (division of Robotics, Perception and LearningDirección de correo verificada de kth.se

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Mårten Björkman

KTH Royal Institute of Technology

Dirección de correo verificada de kth.se

Computer Vision Robotics Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
A framework for vision based bearing only 3D SLAM P Jensfelt, D Kragic, J Folkesson, M Bjorkman Proceedings 2006 IEEE International Conference on Robotics and Automation …, 2006	167	2006
Deep predictive policy training using reinforcement learning A Ghadirzadeh, A Maki, D Kragic, M Björkman 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017	164*	2017
Enhancing visual perception of shape through tactile glances M Bjorkman, Y Bekiroglu, V Hogman, D Kragic Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International …, 2013	162	2013
An active vision system for detecting, fixating and manipulating objects in the real world B Rasolzadeh, M Björkman, K Huebner, D Kragic The International Journal of Robotics Research 29 (2-3), 133-154, 2010	150	2010
Vision for robotic object manipulation in domestic settings D Kragic, M Björkman, HI Christensen, JO Eklundh Robotics and autonomous Systems 52 (1), 85-100, 2005	133	2005
Active 3D scene segmentation and detection of unknown objects M Björkman, D Kragic 2010 IEEE international conference on robotics and automation, 3114-3120, 2010	106	2010
Human-centered collaborative robots with deep reinforcement learning A Ghadirzadeh, X Chen, W Yin, Z Yi, M Björkman, D Kragic IEEE Robotics and Automation Letters 6 (2), 566-571, 2020	91	2020
Detecting, segmenting and tracking unknown objects using multi-label MRF inference M Björkman, N Bergström, D Kragic Computer Vision and Image Understanding 118, 111-127, 2014	72	2014
Meta-learning for multi-objective reinforcement learning X Chen, A Ghadirzadeh, M Björkman, P Jensfelt 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019	70	2019
Deep reinforcement learning to acquire navigation skills for wheel-legged robots in complex environments X Chen, A Ghadirzadeh, J Folkesson, M Björkman, P Jensfelt RSJ International Conference on Intelligent Robots and Systems (IROS), 3110-3116, 2018	62	2018
A sensorimotor reinforcement learning framework for physical human-robot interaction A Ghadirzadeh, J Bütepage, A Maki, D Kragic, M Björkman 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016	62	2016
Combination of foveal and peripheral vision for object recognition and pose estimation M Bjorkman, D Kragic IEEE International Conference on Robotics and Automation, 2004. Proceedings …, 2004	58	2004
Combining planning and learning of behavior trees for robotic assembly J Styrud, M Iovino, M Norrlöf, M Björkman, C Smith 2022 International Conference on Robotics and Automation (ICRA), 11511-11517, 2022	54	2022
Attention-based active 3D point cloud segmentation M Johnson-Roberson, J Bohg, M Björkman, D Kragic 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2010	53	2010
Real-time epipolar geometry estimation of binocular stereo heads M Bjorkman, JO Eklundh IEEE Transactions on pattern analysis and machine intelligence 24 (3), 425-432, 2002	48	2002
An attentional system combining top-down and bottom-up influences B Rasolzadeh, M Björkman, JO Eklundh International Cognitive Vision Workshop (ICVW06), 2006	45	2006
Bridging the gap between emotion and joint action MMN Bieńkiewicz, AP Smykovskyi, T Olugbade, S Janaqi, A Camurri, ... Neuroscience & Biobehavioral Reviews 131, 806-833, 2021	40	2021
Bayesian meta-learning for few-shot policy adaptation across robotic platforms A Ghadirzadeh, X Chen, P Poklukar, C Finn, M Björkman, D Kragic 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021	40*	2021
Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration GZ Gandler, CH Ek, M Björkman, R Stolkin, Y Bekiroglu Robotics and Autonomous Systems 126, 103433, 2020	40	2020
Generating object hypotheses in natural scenes through human-robot interaction N Bergström, M Björkman, D Kragic 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2011	40	2011

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