Curriculum Vitae

Rudolf Lioutikov

Research Interests
Robot learningThe human environment is highly dynamical. Manually programming robots for such an environment is infeasible, hence robots that are supposed to assist in our daily life need the ability to adapt to changing environments.
Human-Robot InteractionRobots which are supposed to actively participate in our daily life, through assistance or service, will work in close proximity to or even collaborate with humans. These robots need to be safe around humans. Furthermore they need to react to humans and even anticipate their actions.
Motion Segmentation & Skill LibrariesTraditional programming of robots is expensive and occasionally infeasible. Learning from Demonstration offers a promising alternative, however demontrating each skill independetedly is very tme consuming and might not be very intuitive. The demontration of an entire task however is very natural. Motion Segmentation and allows the robot to extract the necessary skills into a library of skills.
Current Position
Since 2013Ph.D. student at Intelligent Autonous Systems Group
 Technische Universität Darmstadt, Germany
 Topic: Semi-autonomous learning in human-robot interactive manipulation tasks
 Supervisor: Prof. Dr. J. Peters.
  
Educational Background
2008-2011Master of Science in Computer Science
 Technische Universität Darmstadt, Germany.
 Thesis: "Learning time-dependent feedback policies with model-based policy search" (2013).
 Supervisors: Gerhard Neumann, Jan Peters
2006-2010Bachelor of Science in Computer Science
 Technische Universität Darmstadt, Germany.
 Thesis: "Porting ROS to Windows" (2008) .
 Supervisor: Thomas Lens.
  
Publications

Lioutikov, R.; Maeda, G.; Veiga, F.F.; Kersting, K.; Peters, J. (in press). Learning Attribute Grammars for Movement Primitive Sequencing, International Journal of Robotics Research.   See Details [Details]   BibTeX Reference [BibTex]

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).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Lioutikov, R. (2018). Parsing Motion and Composing Behavior for Semi-Autonomous Manipulation, PhD Thesis.   See Details [Details]   BibTeX Reference [BibTex]

Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Kroemer, O.; Peters, J. (2017). Probabilistic Movement Primitives for Coordination of Multiple Human-Robot Collaborative Tasks, Autonomous Robots (AURO), 41, 3, pp.593-612.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Maeda, G.; Ewerton, M.; Neumann, G.; Lioutikov, R.; Peters, J. (2017). Phase Estimation for Fast Action Recognition and Trajectory Generation in Human-Robot Collaboration, International Journal of Robotics Research (IJRR), 36, 13-14, pp.1579-1594.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Osa, T.; Ghalamzan, E. A. M.; Stolkin, R.; Lioutikov, R.; Peters, J.; Neumann, G. (2017). Guiding Trajectory Optimization by Demonstrated Distributions, IEEE Robotics and Automation Letters (RA-L), 2, 2, pp.819-826, IEEE.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Wilbers, D.; Lioutikov, R.; Peters, J. (2017). Context-Driven Movement Primitive Adaptation, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

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]

Paraschos, A.; Lioutikov, R.; Peters, J.; Neumann, G. (2017). Probabilistic Prioritization of Movement Primitives, Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Maeda, G.; Maloo, A.; Ewerton, M.; Lioutikov, R.; Peters, J. (2016). Proactive Human-Robot Collaboration with Interaction Primitives, International Workshop on Human-Friendly Robotics (HFR), Genoa, Italy.   See Details [Details]   BibTeX Reference [BibTex]

Maeda, G.; Maloo, A.; Ewerton, M.; Lioutikov, R.; Peters, J. (2016). Anticipative Interaction Primitives for Human-Robot Collaboration, AAAI Fall Symposium Series. Shared Autonomy in Research and Practice, Arlington, VA, USA.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Koert, D.; Maeda, G.J.; Lioutikov, R.; Neumann, G.; Peters, J. (2016). Demonstration Based Trajectory Optimization for Generalizable Robot Motions, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Learning Multiple Collaborative Tasks with a Mixture of Interaction Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA), pp.1535--1542.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Lopes, M.; Peters, J.; Piater, J.; Toussaint, M.; Baisero, A.; Busch, B.; Erkent, O.; Kroemer, O.; Lioutikov, R.; Maeda, G.; Mollard, Y.; Munzer, T.; Shukla, D. (2015). Semi-Autonomous 3rd-Hand Robot, Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, Vienna, Austria.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

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]

Rueckert, E.; Lioutikov, R.; Calandra, R.; Schmidt, M.; Beckerle, P.; Peters, J. (2015). Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations, ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Ewerton, M.; Neumann, G.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Maeda, G. (2015). Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives, Workshop on Machine Learning for Social Robotics, ICRA.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Maeda, G.; Neumann, G.; Ewerton, M.; Lioutikov, R.; Peters, J. (2015). A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation, Proceedings of the International Symposium of Robotics Research (ISRR).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Abdolmaleki, A.; Lioutikov, R.; Peters, J; Lau, N.; Reis, L.; Neumann, G. (2015). Model-Based Relative Entropy Stochastic Search, Advances in Neural Information Processing Systems (NIPS), MIT Press.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

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]

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]

Maeda, G.J.; Ewerton, M.; Lioutikov, R.; Amor, H.B.; Peters, J.; Neumann, G. (2014). Learning Interaction for Collaborative Tasks with Probabilistic Movement Primitives, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), pp.527--534.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Lioutikov, R.; Paraschos, A.; Peters, J.; Neumann, G. (2014). Sample-Based Information-Theoretic Stochastic Optimal Control, Proceedings of the International Conference on Robotics and Automation (ICRA).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Lioutikov, R. (2013). Learning time-dependent feedback policies with model-based policy search, Master Thesis.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Lioutikov, R. (2012). Machine learning and the brain, Seminar Thesis, Proceedings of the Robot Learning Seminar.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Reviewing
2014International Conference on Intelligent Robots and Systems (IROS) 2014

  

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