| Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on Robotics 30 (2), 289-309, 2013 | 654 | 2013 |
| Leveraging big data for grasp planning D Kappler, J Bohg, S Schaal 2015 IEEE International Conference on Robotics and Automation (ICRA), 4304-4311, 2015 | 178 | 2015 |
| Interactive perception: Leveraging action in perception and perception in action J Bohg, K Hausman, B Sankaran, O Brock, D Kragic, S Schaal, ... IEEE Transactions on Robotics 33 (6), 1273-1291, 2017 | 143 | 2017 |
| Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010 | 141 | 2010 |
| Opengrasp: a toolkit for robot grasping simulation B León, S Ulbrich, R Diankov, G Puche, M Przybylski, A Morales, T Asfour, ... International Conference on Simulation, Modeling, and Programming for …, 2010 | 120 | 2010 |
| Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks MA Lee, Y Zhu, K Srinivasan, P Shah, S Savarese, L Fei-Fei, A Garg, ... 2019 International Conference on Robotics and Automation (ICRA), 8943-8950, 2019 | 107 | 2019 |
| Learning of grasp selection based on shape-templates A Herzog, P Pastor, M Kalakrishnan, L Righetti, J Bohg, T Asfour, ... Autonomous Robots 36 (1-2), 51-65, 2014 | 102 | 2014 |
| Mind the gap-robotic grasping under incomplete observation J Bohg, M Johnson-Roberson, B León, J Felip, X Gratal, N Bergström, ... 2011 IEEE International Conference on Robotics and Automation, 686-693, 2011 | 100 | 2011 |
| Automatic LQR tuning based on Gaussian process global optimization A Marco, P Hennig, J Bohg, S Schaal, S Trimpe 2016 IEEE international conference on robotics and automation (ICRA), 270-277, 2016 | 78 | 2016 |
| Fusing visual and tactile sensing for 3-D object reconstruction while grasping J Ilonen, J Bohg, V Kyrki 2013 IEEE International Conference on Robotics and Automation, 3547-3554, 2013 | 54 | 2013 |
| Grasping familiar objects using shape context J Bohg, D Kragic 2009 International Conference on Advanced Robotics, 1-6, 2009 | 53 | 2009 |
| 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 | 49 | 2010 |
| Probabilistic articulated real-time tracking for robot manipulation CG Cifuentes, J Issac, M Wüthrich, S Schaal, J Bohg IEEE Robotics and Automation Letters 2 (2), 577-584, 2016 | 47 | 2016 |
| Probabilistic object tracking using a range camera M Wüthrich, P Pastor, M Kalakrishnan, J Bohg, S Schaal 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 45 | 2013 |
| Enhanced visual scene understanding through human-robot dialog M Johnson-Roberson, J Bohg, G Skantze, J Gustafson, R Carlson, ... 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2011 | 44 | 2011 |
| Combining learned and analytical models for predicting action effects A Kloss, S Schaal, J Bohg arXiv preprint arXiv:1710.04102, 2017 | 41 | 2017 |
| Real-time perception meets reactive motion generation D Kappler, F Meier, J Issac, J Mainprice, CG Cifuentes, M Wüthrich, ... IEEE Robotics and Automation Letters 3 (3), 1864-1871, 2018 | 38 | 2018 |
| Direct Loss Minimization Inverse Optimal Control. A Doerr, ND Ratliff, J Bohg, M Toussaint, S Schaal Robotics: Science and Systems, 2015 | 37 | 2015 |
| Robot arm pose estimation through pixel-wise part classification J Bohg, J Romero, A Herzog, S Schaal 2014 IEEE International Conference on Robotics and Automation (ICRA), 3143-3150, 2014 | 34 | 2014 |
| Meteornet: Deep learning on dynamic 3d point cloud sequences X Liu, M Yan, J Bohg Proceedings of the IEEE International Conference on Computer Vision, 9246-9255, 2019 | 31 | 2019 |
Stefan SchaalComputer Scientist in Robotics and AIVerified email at google.com
