Curriculum Vitae

Simon Manschitz

Research Interests
Imitation learningLearning from observing the actions of others is considered a key element of human intelligence. Porting this ability to robots therefore is an important step towards autonomous behavior.
Sequential skill learningBy coordinating basic elementary movements, complex sequential and parallel movement behaviours can be achieved. The goal is to learn when to perform which basic movement in order to perform a complex task.
Current Position
Since 2014Ph.D. student at Intelligent Autonous Systems Group (Technische Universität Darmstadt, Germany) and Honda Research Institute Europe (Offenbach, Germany)
 Topic: Learning sequential skills for robot manipulation tasks
 Supervisors: Prof. Dr. J. Peters., Dr. Jens Kober, and Dr. Michael Gienger
  
Educational Background
2011-2014Master of Science in Informationssystemtechnik (with honors)
 Technische Universität Darmstadt, Germany.
 Thesis: "Learning sequential skills for robot manipulation tasks" (2014).
 Supervisors: Prof. Dr. Jan Peter, Dr. Jens Kober, and Dr. Michael Gienger.
2007-2011Bachelor of Science in Informationssystemtechnik
 Technische Universität Darmstadt, Germany.
 Thesis: "Automated Conversion of Matlab Simulink models into a Hardware Synthesizeable Form" (2011) .
 Supervisors: Prof. Dr. Andreas Koch.
  
Internships
2013Honda Research Institute Europe, Offenbach, Germany.
 Literature studies for learning sequential skills for robot manipulation tasks.
Other Interests
2011-2013Robotics and computer science teacher in a german middle school.
Publications

Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2018). Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations, IEEE Robotics and Automation Letters (RA-L), 3, 2, pp.926-933.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S. (2017). Learning Sequential Skills for Robot Manipulation Tasks, PhD Thesis.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S.; Gienger, M.; Kober, J.; Peters, J. (2016). Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2015). Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations, Robotics and Autonomous Systems, 74, pp.97-107.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S. (2014). Learning Sequential Skills for Robot Manipulation Tasks, Master Thesis.   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Unscrew a Light Bulb from Demonstrations, Proceedings of ISR/ROBOTIK 2014.   See Details [Details]   BibTeX Reference [BibTex]

Manschitz, S.; Kober, J.; Gienger, M.; Peters, J. (2014). Learning to Sequence Movement Primitives from Demonstrations, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   See Details [Details]   Download Article [PDF]   BibTeX Reference [BibTex]

  

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