I have graduated and moved to University of Texas at Austin. You can still reach me via emailrudi@robot-learning.de or my new Homepage .

Rudolf Lioutikov

Rudolf Lioutikov joined the Intelligent Autonomous System lab on November, 1st 2013 as a Ph.D. student. His research includes imitation-learning, skill learning, motion segmentation and human-robot interaction for non-experts. During his Ph.D., Rudolf worked on the 3rd Hand Project where he developed and evaluated new approaches in the field of semi-autonomous human-robot collaboration tasks. Combined, the developed methods form an imitation learning pipeline.

The Probabilisitic Segmentation (ProbS) Δ method segments initially unlabeled demonstrations are segmented into sequences of movement primitives, while simultaneously learning a library of primitives. ProbS is an Expectation Maximization method, where the E-step computes a new segmentation of the demonstrations given the current model and the M-step learns a new model given the current segmentation. The model is defined as a mixture of primitives. The segmented demonstrations and the learned primitive library are used to induce a Probabilistic Context-Free Grammar for primitive sequencing. The grammar structure is learned using an Markov chain Monte Carlo optimization, where a novel prior based on three Poisson distributions was introduced. The induced grammar serves as an easily comprehensible representation of the capability of a robot with respect to the primitives learned from the demonstrations. The grammar can now be used to generate new sequences of primitives from the library. Finally, the induced grammar is improved through reinforcement learning approaches in order to improve the grammar such that previously unseen tasks can be solved by the grammar and the primitive library.

Before his Ph.D., Rudolf completed his Master Degree in Computer Science at the Technische Universitaet Darmstadt focusing on robotics and machine learning with a minor in bionics. In his masters thesis Rudolf investigated robust and safe policy search methods under the supervision of Gerhard Neumann and Jan Peters. He developed a new policy update approach entitled Information Theoretic Stochastic Optimal Control.

Rudolf is also looks into approaches for anticipatory robot behavior, active learning for human-robot interactions, infusing semantic meaning into learned movement grammars, task and role identification in collaborative human-robot scenarios and robot-assisted learning of human motor skills. Another big interest of Rudolf is the application of machine and robot-learning methods in prosthetic devices. Such intelligent prostheses could improve the adaptation process, the capabilities and the quality of life of the respective person.

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Research Interests

Machine-Learning

Imitation Learning, Reinforcement Learning, Policy Search, Movement Primitive Representation, Skill Acquisition, Movement Segmentation, Structure Learning, Grammar Induction, Skill Composition and Sequencing, Life-Long Learning, Active Learning

Robotics

Anthropomorphic Robots, Human-Robot Interaction, Semi-Autonomy, Motor Skills, Adaptive Control, Human-In-The-Loop, Robot Learning

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.   Download Article [PDF]   BibTeX Reference [BibTex]
2. Lioutikov, R.; Maeda, G.; Veiga, F.F.; Kersting, K.; Peters, J. (2018). Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]
3. 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.   Download Article [PDF]   BibTeX Reference [BibTex]