Jens Kober

Alumni

Empirical Inference

Jens Kober is currently working towards a Ph.D. in the area of motor skill learning with a strong focus on learning motor primitives and on reinforcement learning.

Please refer to http://www.robot-learning.de/Member/JensKober for more details.

ICRA 2010 Video

ICRA 2009 Video

NIPS 2008 Videos

IROS 2008 Video

Please refer to http://www.robot-learning.de/Member/CVJensKober.

43 results (View BibTeX file of all listed publications)

2014

Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

2014

ei Kober, J., Peters, J. Learning Motor Skills: From Algorithms to Robot Experiments 97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

Learning to Unscrew a Light Bulb from Demonstrations

Manschitz, S., Kober, J., Gienger, M., Peters, J.

In Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics and Robotik 2014, 2014 (inproceedings)

[BibTex]

ei Manschitz, S., Kober, J., Gienger, M., Peters, J. Learning to Unscrew a Light Bulb from Demonstrations In Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics and Robotik 2014, 2014 (inproceedings)

[BibTex]

2013

Towards Robot Skill Learning: From Simple Skills to Table Tennis

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference on Machine Learning, Part III (ECML 2013), LNCS 8190, pages: 627-631, (Editors: Blockeel, H.,Kersting, K., Nijssen, S., and Zelezný, F.), Springer, 2013 (inproceedings)

PDF DOI [BibTex]

2013

ei Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G. Towards Robot Skill Learning: From Simple Skills to Table Tennis In Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference on Machine Learning, Part III (ECML 2013), LNCS 8190, pages: 627-631, (Editors: Blockeel, H.,Kersting, K., Nijssen, S., and Zelezný, F.), Springer, 2013 (inproceedings)

PDF DOI [BibTex]

Reinforcement Learning in Robotics: A Review

Kober, J., Bagnell, D., Peters, J.

International Journal of Robotics Research, 32(11):1238–1274, 2013 (article)

PDF DOI [BibTex]

ei Kober, J., Bagnell, D., Peters, J. Reinforcement Learning in Robotics: A Review International Journal of Robotics Research, 32(11):1238–1274, 2013 (article)

PDF DOI [BibTex]

Learning to Select and Generalize Striking Movements in Robot Table Tennis

Mülling, K., Kober, J., Kroemer, O., Peters, J.

International Journal of Robotics Research, 32(3):263-279, 2013 (article)

PDF DOI [BibTex]

ei Mülling, K., Kober, J., Kroemer, O., Peters, J. Learning to Select and Generalize Striking Movements in Robot Table Tennis International Journal of Robotics Research, 32(3):263-279, 2013 (article)

PDF DOI [BibTex]

Learning Skills with Motor Primitives

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Proceedings of the 16th Yale Workshop on Adaptive and Learning Systems, 2013 (inproceedings)

[BibTex]

ei Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G. Learning Skills with Motor Primitives In Proceedings of the 16th Yale Workshop on Adaptive and Learning Systems, 2013 (inproceedings)

[BibTex]

2012

Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

PDF [BibTex]

2012

ei Kober, J. Learning Motor Skills: From Algorithms to Robot Experiments Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

PDF [BibTex]

Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract

As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

Web DOI [BibTex]

ei Kober, J., Peters, J. Reinforcement Learning in Robotics: A Survey In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Web DOI [BibTex]

Reinforcement learning to adjust parametrized motor primitives to new situations

Kober, J., Wilhelm, A., Oztop, E., Peters, J.

Autonomous Robots, 33(4):361-379, 2012 (article)

Abstract

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

PDF PDF DOI [BibTex]

ei Kober, J., Wilhelm, A., Oztop, E., Peters, J. Reinforcement learning to adjust parametrized motor primitives to new situations Autonomous Robots, 33(4):361-379, 2012 (article)

PDF PDF DOI [BibTex]

Learning to Select and Generalize Striking Movements in Robot Table Tennis

Mülling, K., Kober, J., Kroemer, O., Peters, J.

In AAAI Fall Symposium on Robots Learning Interactively from Human Teachers, 2012 (inproceedings)

[BibTex]

ei Mülling, K., Kober, J., Kroemer, O., Peters, J. Learning to Select and Generalize Striking Movements in Robot Table Tennis In AAAI Fall Symposium on Robots Learning Interactively from Human Teachers, 2012 (inproceedings)

[BibTex]

Robot Skill Learning

Peters, J., Kober, J., Mülling, K., Nguyen-Tuong, D., Kroemer, O.

In 20th European Conference on Artificial Intelligence , pages: 40-45, ECAI, 2012 (inproceedings)

PDF DOI Project Page [BibTex]

ei Peters, J., Kober, J., Mülling, K., Nguyen-Tuong, D., Kroemer, O. Robot Skill Learning In 20th European Conference on Artificial Intelligence , pages: 40-45, ECAI, 2012 (inproceedings)

PDF DOI Project Page [BibTex]

Learning Throwing and Catching Skills

Kober, J., Mülling, K., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 5167-5168, IROS, 2012 (inproceedings)

PDF DOI [BibTex]

ei Kober, J., Mülling, K., Peters, J. Learning Throwing and Catching Skills In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 5167-5168, IROS, 2012 (inproceedings)

PDF DOI [BibTex]

2011

A biomimetic approach to robot table tennis

Mülling, K., Kober, J., Peters, J.

Adaptive Behavior , 19(5):359-376 , October 2011 (article)

Abstract

Playing table tennis is a difficult motor task that requires fast movements, accurate control and adaptation to task parameters. Although human beings see and move slower than most robot systems, they significantly outperform all table tennis robots. One important reason for this higher performance is the human movement generation. In this paper, we study human movements during table tennis and present a robot system that mimics human striking behavior. Our focus lies on generating hitting motions capable of adapting to variations in environmental conditions, such as changes in ball speed and position. Therefore, we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model was evaluated both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAM™ robot arm.

Jens Kober

ICRA 2010 Video

ICRA 2009 Video

NIPS 2008 Videos

IROS 2008 Video

2014

2014

2013

2013

2012

2012

2011

2011

2010

2010

2009

2009

2008

2008

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