Oliver Kroemer

Alumni

Empirical Inference

oliverkro@tuebingen.mpg.de

Publications

35 results (BibTeX)

2014

Interaction Primitives for Human-Robot Cooperation Tasks

Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 2831-2837, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

2014

Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J. Interaction Primitives for Human-Robot Cooperation Tasks In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 2831-2837, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

Learning to Predict Phases of Manipulation Tasks as Hidden States

Kroemer, O., van Hoof, H., Neumann, G., Peters, J.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 4009-4014, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

Kroemer, O., van Hoof, H., Neumann, G., Peters, J. Learning to Predict Phases of Manipulation Tasks as Hidden States In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 4009-4014, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot

Lioutikov, R., Kroemer, O., Peters, J., Maeda, G.

In Proceedings of the 13th International Conference on Intelligent Autonomous Systems, 302, pages: 1601-1611, Advances in Intelligent Systems and Computing, (Editors: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H.), Springer, IAS, 2014 (inproceedings)

DOI [BibTex]

Lioutikov, R., Kroemer, O., Peters, J., Maeda, G. Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot In Proceedings of the 13th International Conference on Intelligent Autonomous Systems, 302, pages: 1601-1611, Advances in Intelligent Systems and Computing, (Editors: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H.), Springer, IAS, 2014 (inproceedings)

DOI [BibTex]

Active Reward Learning

Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J.

In Proceedings of Robotics: Science & Systems, (Editors: Fox, D., Kavraki, LE., and Kurniawati, H.), RSS, 2014 (inproceedings)

PDF [BibTex]

Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J. Active Reward Learning In Proceedings of Robotics: Science & Systems, (Editors: Fox, D., Kavraki, LE., and Kurniawati, H.), RSS, 2014 (inproceedings)

PDF [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

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]

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]

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]

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

Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (inproceedings)

PDF Web Project Page [BibTex]

2012

Boularias, A., Kroemer, O., Peters, J. Structured Apprenticeship Learning In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (inproceedings)

PDF Web Project Page [BibTex]

Point Cloud Completion Using Symmetries and Extrusions

Kroemer, O., Ben Amor, H., Ewerton, M., Peters, J.

In IEEE-RAS International Conference on Humanoid Robots , pages: 680-685, IEEE, HUMANOIDS, 2012 (inproceedings)

DOI [BibTex]

Kroemer, O., Ben Amor, H., Ewerton, M., Peters, J. Point Cloud Completion Using Symmetries and Extrusions In IEEE-RAS International Conference on Humanoid Robots , pages: 680-685, IEEE, HUMANOIDS, 2012 (inproceedings)

DOI [BibTex]

Generalization of Human Grasping for Multi-Fingered Robot Hands

Ben Amor, H., Kroemer, O., Hillenbrand, U., Neumann, G., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 2043-2050, IROS, 2012 (inproceedings)

PDF DOI [BibTex]

Ben Amor, H., Kroemer, O., Hillenbrand, U., Neumann, G., Peters, J. Generalization of Human Grasping for Multi-Fingered Robot Hands In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 2043-2050, IROS, 2012 (inproceedings)

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]

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]

Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

[BibTex]

Boularias, A., Kroemer, O., Peters, J. Structured Apprenticeship Learning European Workshop on Reinforcement Learning (EWRL), 2012 (talk)

[BibTex]

Multi-modal Learning for Dynamic Tactile Sensing. (IROS 2012 Workshop on Advances in tactile sensing and touch-based human-robot interaction)

Kroemer, O., Lampert, C., Peters, J.

2012 (poster)

[BibTex]

Kroemer, O., Lampert, C., Peters, J. Multi-modal Learning for Dynamic Tactile Sensing. (IROS 2012 Workshop on Advances in tactile sensing and touch-based human-robot interaction) 2012 (poster)

[BibTex]

A Kernel-based Approach to Direct Action Perception

Kroemer, O., Ugur, E., Oztop, E., Peters, J.

In International Conference on Robotics and Automation (ICRA 2012), pages: 2605-2610, IEEE, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

Abstract

The direct perception of actions allows a robot to predict the afforded actions of observed novel objects. In addition to learning which actions are afforded, the robot must also learn to adapt its actions according to the object being manipulated. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.

PDF Web DOI Project Page [BibTex]

Kroemer, O., Ugur, E., Oztop, E., Peters, J. A Kernel-based Approach to Direct Action Perception In International Conference on Robotics and Automation (ICRA 2012), pages: 2605-2610, IEEE, IEEE International Conference on Robotics and Automation (ICRA), May 2012 (inproceedings)

PDF Web DOI Project Page [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]

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]

Maximally Informative Interaction Learning for Scene Exploration

van Hoof, H., Kroemer, O., Ben Amor, H., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 5152-5158, IROS, 2012 (inproceedings)

PDF DOI [BibTex]

van Hoof, H., Kroemer, O., Ben Amor, H., Peters, J. Maximally Informative Interaction Learning for Scene Exploration In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 5152-5158, IROS, 2012 (inproceedings)

PDF DOI [BibTex]

Algorithms for Learning Markov Field Policies

Boularias, A., Kroemer, O., Peters, J.

In Advances in Neural Information Processing Systems 25, pages: 2186-2194, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF [BibTex]

Boularias, A., Kroemer, O., Peters, J. Algorithms for Learning Markov Field Policies In Advances in Neural Information Processing Systems 25, pages: 2186-2194, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

PDF [BibTex]

2011

Active Exploration for Robot Parameter Selection in Episodic Reinforcement Learning

Kroemer, O., Peters, J.

In Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), pages: 25-31, IEEE, Piscataway, NJ, USA, IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011 (inproceedings)

Abstract

As the complexity of robots and other autonomous systems increases, it becomes more important that these systems can adapt and optimize their settings actively. However, such optimization is rarely trivial. Sampling from the system is often expensive in terms of time and other costs, and excessive sampling should therefore be avoided. The parameter space is also usually continuous and multi-dimensional. Given the inherent exploration-exploitation dilemma of the problem, we propose treating it as an episodic reinforcement learning problem. In this reinforcement learning framework, the policy is defined by the system's parameters and the rewards are given by the system's performance. The rewards accumulate during each episode of a task. In this paper, we present a method for efficiently sampling and optimizing in continuous multidimensional spaces. The approach is based on Gaussian process regression, which can represent continuous non-linear mappings from parameters to system performance. We employ an upper confidence bound policy, which explicitly manages the trade-off between exploration and exploitation. Unlike many other policies for this kind of problem, we do not rely on a discretization of the action space. The presented method was evaluated on a real robot. The robot had to learn grasping parameters in order to adapt its grasping execution to different objects. The proposed method was also tested on a more general gain tuning problem. The results of the experiments show that the presented method can quickly determine suitable parameters and is applicable to real online learning applications.

PDF Web DOI [BibTex]

2011

Kroemer, O., Peters, J. Active Exploration for Robot Parameter Selection in Episodic Reinforcement Learning In Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), pages: 25-31, IEEE, Piscataway, NJ, USA, IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011 (inproceedings)

PDF Web DOI [BibTex]

A Non-Parametric Approach to Dynamic Programming

Kroemer, O., Peters, J.

In Advances in Neural Information Processing Systems 24, pages: 1719-1727, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

Abstract

In this paper, we consider the problem of policy evaluation for continuousstate systems. We present a non-parametric approach to policy evaluation, which uses kernel density estimation to represent the system. The true form of the value function for this model can be determined, and can be computed using Galerkin’s method. Furthermore, we also present a unified view of several well-known policy evaluation methods. In particular, we show that the same Galerkin method can be used to derive Least-Squares Temporal Difference learning, Kernelized Temporal Difference learning, and a discrete-state Dynamic Programming solution, as well as our proposed method. In a numerical evaluation of these algorithms, the proposed approach performed better than the other methods.

PDF Web Project Page [BibTex]

Kroemer, O., Peters, J. A Non-Parametric Approach to Dynamic Programming In Advances in Neural Information Processing Systems 24, pages: 1719-1727, (Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger), Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

PDF Web Project Page [BibTex]

Towards Motor Skill Learning for Robotics

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

In Robotics Research, pages: 469-482, (Editors: Pradalier, C. , R. Siegwart, G. Hirzinger), Springer, Berlin, Germany, 14th International Symposium on Robotics Research (ISRR), January 2011 (inproceedings)

Abstract

Learning robots that can acquire new motor skills and refine existing one has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences. Early steps towards this goal in the 1980s made clear that reasoning and human insights will not suffice. Instead, new hope has been offered by the rise of modern machine learning approaches. However, to date, it becomes increasingly clear that off-the-shelf machine learning approaches will not suffice for motor skill learning as these methods often do not scale into the high-dimensional domains of manipulator and humanoid robotics nor do they fulfill the real-time requirement of our domain. As an alternative, we propose to break the generic skill learning problem into parts that we can understand well from a robotics point of view. After designing appropriate learning approaches for these basic components, these will serve as the ingredients of a general approach to motor skill learning. In this paper, we discuss our recent and current progress in this direction. For doing so, we present our work on learning to control, on learning elementary movements as well as our steps towards learning of complex tasks. We show several evaluations both using real robots as well as physically realistic simulations.

PDF Web DOI [BibTex]

Peters, J., Mülling, K., Kober, J., Nguyen-Tuong, D., Kroemer, O. Towards Motor Skill Learning for Robotics In Robotics Research, pages: 469-482, (Editors: Pradalier, C. , R. Siegwart, G. Hirzinger), Springer, Berlin, Germany, 14th International Symposium on Robotics Research (ISRR), January 2011 (inproceedings)

PDF Web DOI [BibTex]

Oliver Kroemer

2014

Interaction Primitives for Human-Robot Cooperation Tasks

2014

Learning to Predict Phases of Manipulation Tasks as Hidden States

Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot

Active Reward Learning

2013

Towards Robot Skill Learning: From Simple Skills to Table Tennis

2013

Learning to Select and Generalize Striking Movements in Robot Table Tennis

Learning Skills with Motor Primitives

2012

Structured Apprenticeship Learning

2012

Point Cloud Completion Using Symmetries and Extrusions

Generalization of Human Grasping for Multi-Fingered Robot Hands

Learning to Select and Generalize Striking Movements in Robot Table Tennis

Structured Apprenticeship Learning

Multi-modal Learning for Dynamic Tactile Sensing. (IROS 2012 Workshop on Advances in tactile sensing and touch-based human-robot interaction)

A Kernel-based Approach to Direct Action Perception

Robot Skill Learning

Maximally Informative Interaction Learning for Scene Exploration

Algorithms for Learning Markov Field Policies

2011

Active Exploration for Robot Parameter Selection in Episodic Reinforcement Learning

2011

A Non-Parametric Approach to Dynamic Programming

Towards Motor Skill Learning for Robotics

Latest News

Useful Links

Contact Us