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Simon Manschitz

Quick Info

Research Interests

Imitation Learning, Sequential Skills, Movement Generation, Machine Learning, Robotics

More Information

Curriculum Vitae Publications Google Citations

Contact Information

Simon Manschitz,
Honda Research Institute Europe,
Carl-Legien-Straße 30,
63073 Offenbach/Main

Simon Manschitz joined the institute for Intelligent Autonomous Systems (IAS) in July 2013 as a Masters student. Since February 2014, he is an IAS Ph.D. student in a joint project with and at the Honda Research

Institute Europe in Offenbach, Germany.

Simon studied Informationssystemtechnik (IST) and received his bachelor degree also from TU Darmstadt. IST is an interdisciplinarily combination of electrical engineering and computer science. Besides robotics, he therefore also got insight into other subjects such as chip design, communication networks/technologies, or software engineering.

The particular focus of Simons Ph.D. project is to learn sequential skills for robot manipulation tasks. By coordinating basic elementary movements, complex sequential and parallel movement behaviours can be achieved. An illustrative example is the replacement of a light bulb: The robot's movement skill can be composed of elementary primitives, such as reaching towards the lamp, aligning the fingers with the bulb, grasping the bulb or turning it in the thread. The sequential skill is coordinating these primitives with a flexible arbitration scheme: It needs to maintain the causal order of the primitives (e.g., reach, pre-shape, grasp), while coordinating the timing of primitives that are active in parallel (co-articulation of left and right hand for bi-manual skills). In case of larger disturbances, the skill needs to adapt the sequential flow to account for the changed situation (e.g., pick up bulb if it drops out of the hand).

Research Interests

Imitation Learning, Sequential Skills, Movement Generation, Machine Learning, Robotics

Key References

  1. 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.   Download Article [PDF]   BibTeX Reference [BibTex]
  2. 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.   Download Article [PDF]   BibTeX Reference [BibTex]
  3. 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).   Download Article [PDF]   BibTeX Reference [BibTex]

A full list of my publications can be found on this page.


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