Bio


Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several awards, most notably the 2019 IEEE International Conference on Robotics and Automation (ICRA) Best Paper Award, the 2019 IEEE Robotics and Automation Society Early Career Award and the 2017 IEEE Robotics and Automation Letters (RA-L) Best Paper Award.

Academic Appointments


2019-20 Courses


Stanford Advisees



  • Peter Zachares
  • Doctoral Dissertation Reader (AC)
    Andrew Bylard, Elena Galbally Herrero, Kunal Menda, Patrick Slade
  • Doctoral Dissertation Advisor (AC)
    Negin Heravi, Michelle Lee, Lin Shao
  • Master's Program Advisor
    Kai Ang, Richard Lin, Yuanhang Luo, Nishant Rai, Dilara Soylu, Kevin Zakka
  • Doctoral Dissertation Co-Advisor (AC)
    Claire Chen, Shushman Choudhury, Mike Salvato
  • Doctoral (Program)
    Toki Migimatsu, Krishnan Srinivasan

All Publications


  • Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects IEEE ROBOTICS AND AUTOMATION LETTERS Yan, M., Zhu, Y., Jin, N., Bohg, J. 2020; 5 (2): 2372–79
  • UniGrasp: Learning a Unified Model to Grasp With Multifingered Robotic Hands IEEE ROBOTICS AND AUTOMATION LETTERS Shao, L., Ferreira, F., Jorda, M., Nambiar, V., Luo, J., Solowjow, E., Ojea, J., Khatib, O., Bohg, J. 2020; 5 (2): 2286–93
  • Learning Task-Oriented Grasping From Human Activity Datasets IEEE ROBOTICS AND AUTOMATION LETTERS Kokic, M., Kragic, D., Bohg, J. 2020; 5 (2): 3352–59
  • Object-Centric Task and Motion Planning in Dynamic Environments IEEE ROBOTICS AND AUTOMATION LETTERS Migimatsu, T., Bohg, J. 2020; 5 (2): 844–51
  • Predicting grasp success in the real world - A study of quality metrics and human assessment ROBOTICS AND AUTONOMOUS SYSTEMS Rubert, C., Kappler, D., Bohg, J., Morales, A. 2019; 121
  • Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Li Fei-Fei, Garg, A., Bohg, J., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 8943–50
  • Leveraging Contact Forces for Learning to Grasp Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 3615–21
  • Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow IEEE ROBOTICS AND AUTOMATION LETTERS Shao, L., Shah, P., Dwaracherla, V., Bohg, J. 2018; 3 (4): 3797–3804
  • Interactive Perception: Leveraging Action in Perception and Perception in Action IEEE TRANSACTIONS ON ROBOTICS Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G. S. 2017; 33 (6): 1273–91
  • Reports on the 2017 AAAI Spring Symposium Series AI MAGAZINE Bohg, J., Boix, X., Chang, N., Chu, V., Churchill, E. F., Fang, F., Feldman, J., Gonzalez, A. J., Kido, T., Lawless, W. F., Montana, J. L., Ontanon, S., Sinapov, J., Sofge, D., Steels, L., Steenson, M., Takadama, K., Yadav, A. 2017; 38 (4): 99–106
  • Probabilistic Articulated Real-Time Tracking for Robot Manipulation IEEE ROBOTICS AND AUTOMATION LETTERS Cifuentes, C., Issac, J., Wuethrich, M., Schaal, S., Bohg, J. 2017; 2 (2): 577–84
  • On the relevance of grasp metrics for predicting grasp success Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J., Bicchi, A., Okamura, A. IEEE. 2017: 265–72
  • Optimizing for what matters: The Top Grasp Hypothesis Kappler, D., Schaal, S., Bohg, J., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 2167–74
  • Exemplar-based Prediction of Global Object Shape from Local Shape Similarity Bohg, J., Kappler, D., Schaal, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 3398–3405
  • Learning Where to Search Using Visual Attention Kloss, A., Kappler, D., Lensch, H. A., Butz, M. V., Schaal, S., Bohg, J., IEEE IEEE. 2016: 5238–45
  • Robust Gaussian Filtering using a Pseudo Measurement Wuethrich, M., Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S., IEEE IEEE. 2016: 3606–13
  • Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles Widmaier, F., Kappler, D., Schaal, S., Bohg, J., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 616–23
  • Automatic LQR Tuning Based on Gaussian Process Global Optimization Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 270–77
  • Depth-Based Object Tracking Using a Robust Gaussian Filter Issac, J., Wuethrich, M., Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 608–15
  • Big Data on Robotics. Big data Bohg, J., Ciocarlie, M., Civera, J., Kavraki, L. E. 2016; 4 (4): 195–96

    View details for DOI 10.1089/big.2016.29013.rob

    View details for PubMedID 27992266

  • Leveraging Big Data for Grasp Planning Kappler, D., Bohg, J., Schaal, S., IEEE IEEE COMPUTER SOC. 2015: 4304–11
  • The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems Wuethrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S., IEEE IEEE COMPUTER SOC. 2015: 2454–61
  • Data-Driven Grasp Synthesis-A Survey IEEE TRANSACTIONS ON ROBOTICS Bohg, J., Morales, A., Asfour, T., Kragic, D. 2014; 30 (2): 289–309
  • Three-dimensional object reconstruction of symmetric objects by fusing visual and tactile sensing INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Ilonen, J., Bohg, J., Kyrki, V. 2014; 33 (2): 321–41
  • Robot Arm Pose Estimation through Pixel-Wise Part Classification Bohg, J., Romero, J., Herzog, A., Schaal, S., IEEE IEEE. 2014: 3143–50
  • Dual Execution of Optimized Contact Interaction Trajectories Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S., IEEE IEEE. 2014: 47–54
  • Fusing Visual and Tactile Sensing for 3-D Object Reconstruction While Grasping Ilonen, J., Bohg, J., Kyrki, V., IEEE IEEE. 2013: 3547–54
  • Probabilistic Object Tracking using a Range Camera Wuethrich, M., Pastor, P., Kalakrishnan, M., Bohg, J., Schaal, S., Amato, N. IEEE. 2013: 3195–3202
  • Visual servoing on unknown objects MECHATRONICS Gratal, X., Romero, J., Bohg, J., Kragic, D. 2012; 22 (4): 423–35
  • Enhanced Visual Scene Understanding through Human-Robot Dialog Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D., IEEE IEEE. 2011: 3342–48
  • Mind the Gap - Robotic Grasping under Incomplete Observation IEEE International Conference on Robotics and Automation Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A. 2011
  • Learning grasping points with shape context ROBOTICS AND AUTONOMOUS SYSTEMS Bohg, J., Kragic, D. 2010; 58 (4): 362–77
  • Attention-based Active 3D Point Cloud Segmentation Johnson-Roberson, M., Bohg, J., Bjorkman, M., Kragic, D., IEEE IEEE. 2010: 1165–70
  • Strategies for Multi-Modal Scene Exploration Bohg, J., Johnson-Roberson, M., Bjorkman, M., Kragic, D., IEEE IEEE. 2010: 4509–15
  • OpenGRASP: A Toolkit for Robot Grasping Simulation Leon, B., Ulbrich, S., Diankov, R., Puche, G., Przybylski, M., Morales, A., Asfour, T., Moisio, S., Bohg, J., Kuffner, J., Dillmann, R., Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., VonStryk, O. SPRINGER-VERLAG BERLIN. 2010: 109–20
  • TOWARDS GRASP-ORIENTED VISUAL PERCEPTION FOR HUMANOID ROBOTS Bohg, J., Barck-Holst, C., Huebner, K., Ralph, M., Rasolzadeh, B., Song, D., Kragic, D. WORLD SCIENTIFIC PUBL CO PTE LTD. 2009: 387–434
  • Integration of Visual Cues for Robotic Grasping Bergstrom, N., Bohg, J., Kragic, D., Fritz, M., Schiele, B., Piater, J. H. SPRINGER-VERLAG BERLIN. 2009: 245–54