Jeannette Bohg

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Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceAdresse e-mail validée de google.com
Danica KragicProfessor of Computer Science, KTH - Royal Institute of TechnologyAdresse e-mail validée de kth.se
Antonio MoralesUniversitat Jaume I, Castellon, SpainAdresse e-mail validée de uji.es
Daniel KapplerMember of Technical Staff, OpenAIAdresse e-mail validée de openai.com
Tamim AsfourKarlsruhe Institute of Technology (KIT)Adresse e-mail validée de kit.edu
Lin ShaoNational University of SingaporeAdresse e-mail validée de nus.edu.sg
Michelle A. LeeStanford UniversityAdresse e-mail validée de cs.stanford.edu
Li Fei-FeiProfessor of Computer Science, Stanford UniversityAdresse e-mail validée de cs.stanford.edu
Krishnan SrinivasanPhD Student, Stanford UniversityAdresse e-mail validée de stanford.edu
Silvio SavareseAssociate Professor of Computer Science at Stanford UniversityAdresse e-mail validée de stanford.edu
Animesh GargGeorgia Institute of Technology, University of Toronto, NvidiaAdresse e-mail validée de gatech.edu
Manuel WüthrichHarvard UniversityAdresse e-mail validée de seas.harvard.edu
Cristina Garcia CifuentesMax Planck Institute for Intelligent SystemsAdresse e-mail validée de tue.mpg.de
Alina KlossNeura RoboticsAdresse e-mail validée de tuebingen.mpg.de
Mengyuan YanStanford UniversityAdresse e-mail validée de stanford.edu
Yuke ZhuAssistant Professor of Computer Science, The University of Texas at AustinAdresse e-mail validée de cs.utexas.edu
Nathan RatliffNVIDIAAdresse e-mail validée de nvidia.com
Sebastian TrimpeProfessor, RWTH Aachen UniversityAdresse e-mail validée de dsme.rwth-aachen.de
Jiajun WuStanford UniversityAdresse e-mail validée de cs.stanford.edu
Matthew Johnson-RobersonProfessor of Robotics, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu

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Jeannette Bohg

Assistant Professor, Stanford University

Adresse e-mail validée de stanford.edu - Page d'accueil

Robotics Multi-Modal Perception Machine Learning Computer Vision Grasping and Manipulation


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	3675	2021
Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on robotics 30 (2), 289-309, 2013	1227	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	579*	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	339	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	322	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	257	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	243	2019
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	231	2023
Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010	223*	2010
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	207	2022
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	205	2019
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	203	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	197	2016
Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations JB Lin Shao, Toki Migimatsu, Qiang Zhang, Kaiyuan Yang Robotics: Science and Systems, 2020	184*	2020
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	176	2023
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	170	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	162	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	152	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	132	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	116*	2014

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