| Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on robotics 30 (2), 289-309, 2013 | 894 | 2013 |
| Making sense of vision and touch: Learning multimodal representations for contact-rich tasks MA Lee, Y Zhu, P Zachares, M Tan, K Srinivasan, S Savarese, L Fei-Fei, ... IEEE Transactions on Robotics 36 (3), 582-596, 2020 | 276* | 2020 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 248 | 2021 |
| Leveraging big data for grasp planning D Kappler, J Bohg, S Schaal 2015 IEEE international conference on robotics and automation (ICRA), 4304-4311, 2015 | 228 | 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 | 210 | 2017 |
| Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010 | 203* | 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 | 146 | 2010 |
| Learning of grasp selection based on shape-templates A Herzog, P Pastor, M Kalakrishnan, L Righetti, J Bohg, T Asfour, ... Autonomous Robots 36 (1), 51-65, 2014 | 129 | 2014 |
| 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 | 120 | 2016 |
| 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 | 118 | 2011 |
| Meteornet: Deep learning on dynamic 3d point cloud sequences X Liu, M Yan, J Bohg Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 101 | 2019 |
| Three-dimensional object reconstruction of symmetric objects by fusing visual and tactile sensing J Ilonen, J Bohg, V Kyrki The International Journal of Robotics Research 33 (2), 321-341, 2014 | 92* | 2014 |
| Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks R Martín-Martín, MA Lee, R Gardner, S Savarese, J Bohg, A Garg 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 83 | 2019 |
| Combining learned and analytical models for predicting action effects from sensory data A Kloss, S Schaal, J Bohg The International Journal of Robotics Research, 0278364920954896, 2020 | 75 | 2020 |
| Probabilistic 3d multi-modal, multi-object tracking for autonomous driving H Chiu, J Li, R Ambruş, J Bohg 2021 IEEE International Conference on Robotics and Automation (ICRA), 14227 …, 2021 | 72 | 2021 |
| 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 | 70 | 2013 |
| Self-supervised learning of state estimation for manipulating deformable linear objects M Yan, Y Zhu, N Jin, J Bohg IEEE robotics and automation letters 5 (2), 2372-2379, 2020 | 69 | 2020 |
| 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 | 68 | 2018 |
| 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 | 66 | 2016 |
| Depth-based object tracking using a robust gaussian filter J Issac, M Wüthrich, CG Cifuentes, J Bohg, S Trimpe, S Schaal 2016 IEEE International Conference on Robotics and Automation (ICRA), 608-615, 2016 | 55 | 2016 |
Stefan SchaalRobotics, Machine Learning, AI, Neurosciencegoogle.com의 이메일 확인됨
