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Mårten Björkman
Tytuł
Cytowane przez
Cytowane przez
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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
1662006
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
1612013
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
160*2017
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
1492010
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
1322005
Active 3D scene segmentation and detection of unknown objects
M Björkman, D Kragic
2010 IEEE international conference on robotics and automation, 3114-3120, 2010
1062010
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
852020
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
702014
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
682019
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
612016
Deep reinforcement learning to acquire navigation skills for wheel-legged robots in complex environments. In 2018 IEEE
X Chen, A Ghadirzadeh, J Folkesson, M Björkman, P Jensfelt
RSJ International Conference on Intelligent Robots and Systems (IROS), 3110-3116, 2018
58*2018
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
582004
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
532010
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
522022
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
472002
An attentional system combining top-down and bottom-up influences
B Rasolzadeh, M Björkman, JO Eklundh
International Cognitive Vision Workshop (ICVW06), 2006
452006
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
402011
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
392020
Vision in the real world: Finding, attending and recognizing objects
M Björkman, JO Eklundh
International Journal of Imaging Systems and Technology 16 (5), 189-208, 2006
392006
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
38*2021
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