| 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 | 4996 | 2021 |
| Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on robotics 30 (2), 289-309, 2013 | 1276 | 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 | 642* | 2020 |
| Open x-embodiment: Robotic learning datasets and rt-x models A O'Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, A Padalkar, A Lee, ... arXiv preprint arXiv:2310.08864, 2023 | 458* | 2023 |
| 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 | 367 | 2017 |
| Leveraging big data for grasp planning D Kappler, J Bohg, S Schaal 2015 IEEE international conference on robotics and automation (ICRA), 4304-4311, 2015 | 334 | 2015 |
| Tidybot: Personalized robot assistance with large language models J Wu, R Antonova, A Kan, M Lepert, A Zeng, S Song, J Bohg, ... Autonomous Robots 47 (8), 1087-1102, 2023 | 328 | 2023 |
| 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 | 286 | 2021 |
| Text2motion: From natural language instructions to feasible plans K Lin, C Agia, T Migimatsu, M Pavone, J Bohg Autonomous Robots 47 (8), 1345-1365, 2023 | 278 | 2023 |
| 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 | 260 | 2019 |
| Vision-only robot navigation in a neural radiance world M Adamkiewicz, T Chen, A Caccavale, R Gardner, P Culbertson, J Bohg, ... IEEE Robotics and Automation Letters 7 (2), 4606-4613, 2022 | 259 | 2022 |
| Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010 | 227* | 2010 |
| 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 | 220 | 2019 |
| 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 | 217 | 2016 |
| Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations JB Lin Shao, Toki Migimatsu, Qiang Zhang, Kaiyuan Yang Robotics: Science and Systems, 2020 | 198* | 2020 |
| 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 | 178 | 2020 |
| Opengrasp: a toolkit for robot grasping simulation B León, S Ulbrich, R Diankov, G Puche, M Przybylski, A Morales, T Asfour, ... Simulation, Modeling, and Programming for Autonomous Robots: Second …, 2010 | 172 | 2010 |
| Learning of grasp selection based on shape-templates A Herzog, P Pastor, M Kalakrishnan, L Righetti, J Bohg, T Asfour, ... Autonomous Robots 36, 51-65, 2014 | 159 | 2014 |
| Deep learning approaches to grasp synthesis: A review R Newbury, M Gu, L Chumbley, A Mousavian, C Eppner, J Leitner, J Bohg, ... IEEE Transactions on Robotics 39 (5), 3994-4015, 2023 | 157 | 2023 |
| 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 | 136 | 2011 |
Stefan SchaalRobotics, Machine Learning, AI, Neuroscience在 google.com 的电子邮件经过验证