Jeannette Bohg

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Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceDirección de correo verificada de google.com
Danica KragicProfessor of Computer Science, KTH - Royal Institute of TechnologyDirección de correo verificada de kth.se
Daniel KapplerMember of Technical Staff, OpenAIDirección de correo verificada de openai.com
Antonio MoralesUniversitat Jaume I, Castellon, SpainDirección de correo verificada de uji.es
Tamim AsfourKarlsruhe Institute of Technology (KIT)Dirección de correo verificada de kit.edu
Michelle A. LeeStanford UniversityDirección de correo verificada de cs.stanford.edu
Silvio SavareseAssociate Professor of Computer Science at Stanford UniversityDirección de correo verificada de stanford.edu
Manuel WüthrichEconCS, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Li Fei-FeiProfessor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Krishnan SrinivasanPhD Student, Stanford UniversityDirección de correo verificada de stanford.edu
Alina KlossNeura RoboticsDirección de correo verificada de tuebingen.mpg.de
Jan IssacMax Planck Institute for Intelligent SystemsDirección de correo verificada de tuebingen.mpg.de
Cristina Garcia CifuentesMax Planck Institute for Intelligent SystemsDirección de correo verificada de tue.mpg.de
Animesh GargAssistant Professor, University of Toronto, Vector Institute, NvidiaDirección de correo verificada de cs.toronto.edu
Mengyuan YanStanford UniversityDirección de correo verificada de stanford.edu
Nathan RatliffNVIDIADirección de correo verificada de nvidia.com
Matthew Johnson-RobersonProfessor of Robotics, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu
Sebastian TrimpeProfessor, RWTH Aachen UniversityDirección de correo verificada de dsme.rwth-aachen.de
Ludovic RighettiNew York UniversityDirección de correo verificada de nyu.edu
Yuke ZhuAssistant Professor of Computer Science, The University of Texas at AustinDirección de correo verificada de cs.utexas.edu

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Jeannette Bohg

Assistant Professor, Stanford University

Dirección de correo verificada de stanford.edu - Página principal

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


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	1116	2021
Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on robotics 30 (2), 289-309, 2013	1032	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	405*	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	270	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	268	2015
Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010	215*	2010
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	157	2010
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	153	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	149	2016
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	142	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	140	2019
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	124	2011
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	106	2020
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	103*	2014
Probabilistic 3d multi-object tracking for autonomous driving H Chiu, A Prioletti, J Li, J Bohg arXiv preprint arXiv:2001.05673, 2020	102	2020
Combining learned and analytical models for predicting action effects A Kloss, S Schaal, J Bohg arXiv preprint arXiv:1710.04102 11, 2017	92*	2017
Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations JB Lin Shao, Toki Migimatsu, Qiang Zhang, Kaiyuan Yang Robotics: Science and Systems, 2020	90*	2020
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	82	2016
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	81	2018
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	79	2013

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