Learning to Grasp

Overview

Traditionally, the problem of robotic grasping has been formalized under the assumption of perfect knowledge on the object, robot hand and their relative pose. Simplifying assumptions were made on contact models, hand kinematics and capabilities or the structure of the environment. While this allows elegant solutions to multi-contact planning, many of these assumptions do not translate well into the real world that is riddled by uncertainty.

We have worked on the problem of how a robot can learn how to grasp when only partial and noisy information is available on the object, robot hand and their relative pose. We proposed different feature representations, learning mechanisms and training data.

Check out the papers below on different proposals for feature representations, learning mechanisms and training data. Read more about approaches towards Learning to Grasp in this comprehensive survey!

2019

Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L. , Bohg, J. Leveraging Contact Forces for Learning to Grasp. Accepted at ICRA '19.

2017

Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J. On the relevance of grasp metrics for predicting grasp success In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems, September 2017.

2016

Kappler, D., Schaal, S., Bohg, J. Optimizing for what matters: the Top Grasp Hypothesis In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016.

Bohg, J., Kappler, D., Schaal, S. Exemplar-based Prediction of Object Properties from Local Shape Similarity In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016.

2015

Kappler, D., Bohg, B., Schaal, S. Leveraging Big Data for Grasp Planning In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015.

2014

Herzog, A., Pastor, P., Kalakrishnan, M., Righetti, L., Bohg, J., Asfour, T., Schaal, S. Learning of Grasp Selection based on Shape-Templates Autonomous Robots, 36(1-2):51-65, Springer US, January 2014.

Bohg, J., Morales, A., Asfour, T., Kragic, D. Data-Driven Grasp Synthesis - A Survey IEEE Transactions on Robotics, 30, pages: 289 - 309, IEEE, April 2014.

2012

Bohg, Jeannette, Welke, Kai, León, Beatriz, Do, Martin, Song, Dan, Wohlkinger, Walter, Aldoma, Aitor, Madry, Marianna, Przybylski, Markus, Asfour, Tamim, Marti, Higinio, Kragic, Danica, Morales, Antonio, Vincze, Markus Task-Based Grasp Adaptation on a Humanoid Robot In 10th IFAC Symposium on Robot Control, SyRoCo 2012, Dubrovnik, Croatia, September 5-7, 2012., pages: 779-786, 2012.

2011

Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A. Mind the gap - robotic grasping under incomplete observation In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages: 686-693, May 2011.

2010

Bohg, J., Kragic, D. Learning Grasping Points with Shape Context Robotics and Autonomous Systems, 58(4):362-377, North-Holland Publishing Co., Amsterdam, The Netherlands, The Netherlands, April 2010.

León, B., Ulbrich, S., Diankov, R., Puche, G., Przybylski, M., Morales, A., Asfour, T., Moisio, S., Bohg, J. Kuffner, J., Dillmann, R. OpenGRASP: A Toolkit for Robot Grasping Simulation In SIMPAR 2010: Simulation, Modeling, and Programming for Autonomous Robots , pages: 109-120, 2010. Best Paper Award.

2009

Bohg, J., Barck-Holst, C., Huebner, K., Ralph, M., Rasolzadeh, B., Song, D., Kragic, D. Towards Grasp-Oriented Visual Perception of Humanoid Robots International Journal of Humanoid Robotics, 06(03):387-434, 2009.

Bohg, J., Kragic, D. Grasping familiar objects using shape context In Advanced Robotics, 2009. ICAR 2009. International Conference on, pages: 1-6, 2009.

Bergström, N., Bohg, J., Kragic, D. Integration of Visual Cues for Robotic Grasping In Computer Vision Systems, 5815, pages: 245-254, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2009.