Jeannette Bohg

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

Jeannette Bohg

Assistant Professor, Stanford University

Verified email at stanford.edu - Homepage

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


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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	1056	2021
Data-driven grasp synthesis—a survey J Bohg, A Morales, T Asfour, D Kragic IEEE Transactions on robotics 30 (2), 289-309, 2013	1026	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	399*	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	268	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	266	2015
Learning grasping points with shape context J Bohg, D Kragic Robotics and Autonomous Systems 58 (4), 362-377, 2010	214*	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	155	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	152	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	139	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	137	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	103	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	100	2020
Combining learned and analytical models for predicting action effects A Kloss, S Schaal, J Bohg arXiv preprint arXiv:1710.04102 11, 2017	91*	2017
Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations JB Lin Shao, Toki Migimatsu, Qiang Zhang, Kaiyuan Yang Robotics: Science and Systems, 2020	88*	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	80	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	80	2016
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	78	2013

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