Rudolf Lioutikov

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Research Interests

Machine Learning, Robotics, Human-Robot Interaction, Optimal Control, Motor-Skill Learning, Automated Segmentation of complex movements, Automated Compilation of Movement Primitives

More Information

Curriculum Vitae Publications Google Citations Code

Contact Information

Mail. Rudolf Lioutikov,
TU Darmstadt, FG IAS,
Hochschulstr. 10, 64289 Darmstadt
Office. Room E325, Building S2|02

Rudolf Lioutikov joined the Intelligent Autonomous System lab on November, 1st 2013 as a PhD student. His research will amongst others include robust and safe policy search methods, skill learning, human-robot interaction and intention learning. During his PhD, Rudolf is working on the 3rd Hand Project where he will develop and evaluate new methods in the field of semi-autonomous human-robot interaction tasks.

Before his PhD, Rudolf completed his Master Degree in Computer Science at the Technische Universitaet Darmstadt. His thesis entitled "Learning time-dependent feedback policies with model-based policy search" was written under the supervision of Gerhard Neumann and Jan Peters.

Teaching tasks between humans often do not only include the demonstration of a task, but also the correcting and guiding of the executed task. These behaviours can be observed between a parent and a child, a trainer and an athlete and an Instructor and a novice worker. Introducing such additional feedback to a robot can improve it's learning speed and the quality of the resulting policies significantly. This form of interactive learning becomes even more important once the desired task includes a human. Forcing the human to act exactly in the same way and solve a task always in precisely the same way and order is not desirable or even possible. Therefore the robot needs to be able to adapt and interact with the human, and be able to incorporate changes into its behaviour which may vary between each interacting human. However if the robot is able to adapt to the human he can increase the humans productivity, and help him to be more efficient in areas where the creativity and and intelligence of a human can not be replaced by a robot.

The research of semi-autonomous robotics offers many challenges in various areas, of which one is the learning and executing of motor skills. The representation of such skills is still a highly researched topic, which lead to various interesting approaches, such as Dynamic Movement Primitives, Interaction Primitives and Probabilistic Movement Primitives. The application and evaluation of such representations in complex human-robot interactions tasks is an important aspect. At the same time a complex task might require the segmentation of the task into several sub-tasks. These segments allow for more adaptability to changes in the scenario. Instead of manually defining and sequencing such segments it is highly desirable to automatically identify useful, reoccurring motions and store them in a skill library. This library could then be used to automatically find the optimal sequence in order to solve a specified task. The automated segmentation and subsequent compilation of complex tasks are important but unfortunately little researched topics. Further research, new methods and approaches and the evaluation on human-robot interaction tasks in this promising topics is therefore necessary.

Research Interests

Machine Learning, Robotics, Human-Robot Interaction, Optimal Control, Motor-Skill Learning, Automated Segmentation of complex movements, Automated Compilation of Movement Primitives.

Key References

  1. Lioutikov, R.; Neumann, G.; Maeda, G.; Peters, J. (2017). Learning Movement Primitive Libraries through Probabilistic Segmentation, International Journal of Robotics Research (IJRR), 36, 8, pp.879-894.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  2. Lioutikov, R.; Neumann, G.; Maeda, G.J.; Peters, J. (2015). Probabilistic Segmentation Applied to an Assembly Task, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  3. Lioutikov, R.; Kroemer, O.; Peters, J.; Maeda, G. (2014). Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot, Proceedings of the 13th International Conference on Intelligent Autonomous Systems (IAS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]
  4. Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Generalizing Movements with Information Theoretic Stochastic Optimal Control, Journal of Aerospace Information Systems, 11, 9, pp.579-595.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]


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