Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at MPI until September 2017 and remains affiliated as a guest researcher. Her research focuses on perception 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.
For more details, check out the group webpage!
Before joining the Autonomous Motion lab in January 2012, Jeannette Bohg was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. Her thesis on Multi-modal scene understanding for Robotic Grasping was performed under the supervision of Prof. Danica Kragic. She 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.
Computer Vision Grasping and Manipulation Machine Learning Humanoid Robotics
This video shows the performance of our fully integrated manipulation system that consumes continuous visual feedback on the environment and thereby adapts motion plans online.
This video showcases a method which optimizes trajectories that are in contact with the environment to exploit these constraints for more robust reaching of a given target. It re-plans these trajectories online using force feedback.
This video showcases a novel variant of the particle filter in which re-sampling is not only done each time-step but also for each dimension of the state vector. Compared to a standard particle filter it yields more robust results the higher the dimension of the state.
We show a visual object tracking method that is specifically well suited for tracking during manipulation tasks. These are situations that are characterized by strong occlusions of the object and are therefore challenging for standard tracking methods. By explicitly modelling these occlusions and some clever factorization of the resulting system state, we developed a methods that has been successfully applied in the Phase 2 of the DARPA ARM Challenge.
Hand-eye coordination is a notorious problem for many robot systems. Even offline calibration may not always provide the final solution or sufficient accuracy for fine manipulation tasks or just simple precision grasps. Being able to estimate the arm pose directly in the image would provide a solution to this problem by providing hand-eye coordination online. We present a frame-by-frame technique for arm pose estimation that does not require an initialization or any information from sensors other than an RGB-D camera.
We present the integration of many different modules to allow a robot to infer a task-relevant grasp for a perceived object. It relies on learning techniques to determine the category of an object and the associated grasp given a task. The system is demonstrated on the humanoid robot Armar IIIa at the Karlsruhe Institute for Technology.
We have developed algorithms which enable an autonomous manipulation system to grasp a wide range of objects and to perform a certain number of manipulation tasks, such as drilling, using a stapler, unlocking a door with a key or changing a tire \cite{Righetti_AR_2013}. More generally, we are interested in providing complete integra...
Ludovic Righetti Mrinal Kalakrishnan Peter Pastor Manuel Wüthrich Alexander Herzog Jeannette Bohg Stefan Schaal
Autonomous systems such as humanoid robots are characterized by a multitude of feedback control loops operating at different hierarchical levels and time-scales. Designing and tuning these controllers typically requires significant manual modeling and design effort and exhaustive experimental testing. For managing the ever greater c...
Alonso Marco Valle Sebastian Trimpe Philipp Hennig Alexander von Rohr Jeannette Bohg Stefan Schaal
Decision making requires knowledge of some variables of interest. In the vast majority of real-world problems, these variables are latent, i.e. they cannot be observed directly and must be inferred from available measurements. To maintain an up-to-date distribution over the latent variables, past beliefs have to ...
Manuel Wüthrich Sebastian Trimpe Cristina Garcia Cifuentes Jan Issac Daniel Kappler Franzi Meier Jeannette Bohg Stefan Schaal
Searching for a given target object in a scene not only requires detecting the target object if it is visible, but also to identify promising locations for search if not. The quantity that measures how interesting an image region is given a certain task is called top-down saliency.
Saliency predictions are mostly used to r...
Motion generation is increasingly formalized as a large scale optimization over future outcomes of actions. For high dimensional manipulation platforms, this optimization is computationally so difficult that for a long time traditional approaches focused primarily on feasibility of the solution rather than even local optimality. Rec...
Nathan Ratliff Ludovic Righetti Jim Mainprice Jeannette Bohg Stefan Schaal
Hand-eye coordination is crucial for capable manipulation of objects. It requires to know the manipulator's and the objects' locations. These locations have to be inferred from sensory data. In this project we work with range sensors, which are wide spread in robotics and provide dense depth images.
...Manuel Wüthrich Jan Issac Cristina Garcia Cifuentes Jeannette Bohg Claudia Pfreundt Peter Pastor Mrinal Kalakrishnan Daniel Kappler Sebastian Trimpe Franzi Meier Stefan Schaal
Autonomous robotic grasping is one of the pre-requisites for personal robots to become useful when assisting humans in households. Seamlessly easy for humans, it still remains a very challenging task for robots. The key problem of robotic grasping is to automatically choose an appropriate grasp configuration given an object as perce...
Alexander Herzog Peter Pastor Mrinal Kalakrishnan Ludovic Righetti Jeannette Bohg Stefan Schaal
While there exist many solutions and criteria for selecting the best grasp for an object of known shape, grasping an object whose shape is uncertain and noise remains a challenge. In this project, we consider the problem of object shape estimation when it is only partially observable. Once we have a prediction, we can apply the crit...
There is compelling evidence that perception in humans and animals is an active and exploratory process. For example, Gibson showed that physical interaction further augments perceptual processing beyond what can be achieved by just looking at the environment. In the specific experiment, human subjects had to find a reference object...
Data-driven methods towards grasping address the challenges of grasp synthesis that arise in the real world such as noisy sensors and incomplete information about the objects and the environment. They focus on finding a suitable representation of the perceptual data that allows to predict whether a certain grasp will succeed.
...We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. Our approach emphasizes the importance of continuous, real-time perception and its tight integration with reactive motion genera...
Jeannette Bohg Daniel Kappler Franzi Meier Jan Issac Jim Mainprice Cristina Garcia Cifuentes Manuel Wüthrich Vincent Berenz Stefan Schaal Nathan Ratliff
Purposeful and robust manipulation requires a good hand-eye coordination. To a certain extend this can be achieved using information from joint encoders and known kinematics. However, for many robots a significant error in the pose of the end-effector and fingers of several centimeters remains. Especially for fine manipulation tasks...
Jeannette Bohg Alexander Herzog Stefan Schaal Javier Romero Felix Widmaier
Shao, L., Shah, P., Dwaracherla, V., Bohg, J.
IEEE Robotics and Automation Letters, 3(4):3797-3804, IEEE, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2018 (conference)
am
Shao, L., Tian, Y., Bohg, J.
arXiv, September 2018, Submitted to ICRA'19 (article) Submitted
am
Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J.
arXiv, September 2018, Submitted to ICRA'19 (article) Submitted
am mg
(Best Systems Paper Finalists - Amazon Robotics Best Paper Awards in Manipulation)
Kappler, D., Meier, F., Issac, J., Mainprice, J., Garcia Cifuentes, C., Wüthrich, M., Berenz, V., Schaal, S., Ratliff, N., Bohg, J.
IEEE Robotics and Automation Letters, 3(3):1864-1871, July 2018 (article)
am
Kloss, A., Schaal, S., Bohg, J.
arXiv, 2018 (article) Submitted
am
Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G.
IEEE Transactions on Robotics, 33, pages: 1273-1291, December 2017 (article)
am
Li, W., Bohg, J., Fritz, M.
arXiv, November 2017 (article) Submitted
am
Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J.
In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017 (inproceedings) Accepted
am
(Best Paper of RA-L 2017, Finalist of Best Robotic Vision Paper Award of ICRA 2017)
Garcia Cifuentes, C., Issac, J., Wüthrich, M., Schaal, S., Bohg, J.
IEEE Robotics and Automation Letters (RA-L), 2(2):577-584, April 2017 (article)
am
Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., Bohg, J.
Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)
am
Bohg, J., Kappler, D., Meier, F., Ratliff, N., Mainprice, J., Issac, J., Wüthrich, M., Garcia Cifuentes, C., Berenz, V., Schaal, S.
In International Workshop on Robotics in the 21st century: Challenges and Promises, September 2016 (inproceedings)am
Wüthrich, M., Garcia Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S.
In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)
am ics
Widmaier, F., Kappler, D., Schaal, S., Bohg, J.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Kappler, D., Schaal, S., Bohg, J.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Bohg, J., Kappler, D., Schaal, S.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am
Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am ics pn
Issac, J., Wüthrich, M., Garcia Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S.
In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)
am ics
Dominey, P. F., Prescott, T. J., Bohg, J., Engel, A. K., Gallagher, S., Heed, T., Hoffmann, M., Knoblich, G., Prinz, W., Schwartz, A.
In The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science, 18, pages: 333-356, 20, Strüngmann Forum Reports, vol. 18, J. Lupp, series editor, (Editors: Andreas K. Engel and Karl J. Friston and Danica Kragic), The MIT Press, 18th Ernst Strüngmann Forum, May 2016 (incollection) In press
am
Bohg, J., Kragic, D.
In The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science, 18, pages: 309-320, 18, Strüngmann Forum Reports, vol. 18, J. Lupp, series editor, (Editors: Andreas K. Engel and Karl J. Friston and Danica Kragic), The MIT Press, 18th Ernst Strüngmann Forum, May 2016 (incollection) In press
am
Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.
Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)
am ei ics pn
Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.
In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)
am ics
Kappler, D., Bohg, B., Schaal, S.
In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)
am
Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S.
In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)
am
Sankaran, B., Bohg, J., Ratliff, N., Schaal, S.
In Reinforcement Learning and Decision Making, 2015 (inproceedings)
am
Bohg, J., Romero, J., Herzog, A., Schaal, S.
In IEEE International Conference on Robotics and Automation (ICRA) 2014, pages: 3143-3150, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (inproceedings)
am ps
Bohg, J., Morales, A., Asfour, T., Kragic, D.
IEEE Transactions on Robotics, 30, pages: 289 - 309, IEEE, April 2014 (article)
am
Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S.
In 2014 IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 47-54, IEEE, Chicago, USA, 2014 (inproceedings)
am mg
Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S.
Autonomous Robots, 36(1-2):51-65, January 2014 (article)
am mg
Wüthrich, M., Pastor, P., Kalakrishnan, M., Bohg, J., Schaal, S.
In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 3195-3202, IEEE, November 2013 (inproceedings)
am
Illonen, J., Bohg, J., Kyrki, V.
The International Journal of Robotics Research, 33(2):321-341, Sage, October 2013 (article)
am
Ilonen, J., Bohg, J., Kyrki, V.
In IEEE International Conference on Robotics and Automation (ICRA), pages: 3547-3554, 2013 (inproceedings)
am
Hutter, M., Bloesch, M., Buchli, J., Semini, C., Bazeille, S., Righetti, L., Bohg, J.
In 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-4, IEEE, Linköping, Sweden, 2013 (inproceedings)mg
Willaert, B., Bohg, J., Van Brussel, H., Niemeyer, G.
In IEEE International Workshop on Haptic Audio Visual Environments and Games (HAVE), pages: 25-31, October 2012 (inproceedings)
am
Bohg, J., Welke, K., León, B., Do, M., Song, D., Wohlkinger, W., Aldoma, A., Madry, M., Przybylski, M., Asfour, T., Marti, H., Kragic, D., Morales, A., Vincze, M.
In 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, September 5-7, 2012., pages: 779-786, September 2012 (inproceedings)
am
Gratal, X., Romero, J., Bohg, J., Kragic, D.
Mechatronics, 22(4):423-435, Elsevier, June 2012, Visual Servoing \{SI\} (article)
am ps
am
Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A.
In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011 (inproceedings)
am
Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D.
In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages: 3342-3348, 2011 (inproceedings)
am
Johnson-Roberson, M., Bohg, J., Kragic, D., Skantze, G., Gustafson, J., Carlson, R.
In Proceedings of AAAI 2010 Fall Symposium: Dialog with Robots, November 2010 (inproceedings)am
Gratal, X., Bohg, J., Björkman, M., Kragic, D.
In IROS’10 Workshop on Defining and Solving Realistic Perception Problems in Personal Robotics, October 2010 (inproceedings)
am
Bohg, J., Johnson-Roberson, M., Björkman, M., Kragic, D.
In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pages: 4509-4515, October 2010 (inproceedings)
am
Johnson-Roberson, M., Bohg, J., Björkman, M., Kragic, D.
In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pages: 1165-1170, October 2010 (inproceedings)
am
Bohg, J., Kragic, D.
Robotics and Autonomous Systems, 58(4):362-377, North-Holland Publishing Co., Amsterdam, The Netherlands, The Netherlands, April 2010 (article)
am
Bohg, J., Kragic, D.
In Advanced Robotics, 2009. ICAR 2009. International Conference on, pages: 1-6, 2009 (inproceedings)
am
Bergström, N., Bohg, J., Kragic, D.
In Computer Vision Systems, 5815, pages: 245-254, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2009 (incollection)
am
Bohg, J., Barck-Holst, C., Huebner, K., Ralph, M., Rasolzadeh, B., Song, D., Kragic, D.
International Journal of Humanoid Robotics, 06(03):387-434, 2009 (article)
am