Jeannette Bohg
Cited by
Cited by
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
Data-driven grasp synthesis—a survey
J Bohg, A Morales, T Asfour, D Kragic
IEEE Transactions on robotics 30 (2), 289-309, 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
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
Leveraging big data for grasp planning
D Kappler, J Bohg, S Schaal
2015 IEEE international conference on robotics and automation (ICRA), 4304-4311, 2015
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
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
Learning grasping points with shape context
J Bohg, D Kragic
Robotics and Autonomous Systems 58 (4), 362-377, 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
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
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
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
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
Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations
JB Lin Shao, Toki Migimatsu, Qiang Zhang, Kaiyuan Yang
Robotics: Science and Systems, 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
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
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
Open x-embodiment: Robotic learning datasets and rt-x models
A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ...
arXiv preprint arXiv:2310.08864, 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
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
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