Robotics, Physics Simulations,
Machine Learning, Automatic Control
Mail. Fabio Muratore
TU Darmstadt, Fachgebiet IAS
Honda Research Institute Europe
63073 Offenbach am Main
Office. Room E323,
There is a large consent both in academia as well as in industry that physical human-robot interaction is attributed a large potential for future robotic applications. While being a generic technology, applications emerge increasingly in the factory domain, particular in the production and assembly processes.
Learning concepts for manipulation tasks is, however still rather academic as it is, imposing a number of assumptions on the underlying problem, and requiring scientists to produce the results. In particular, learning of force-based manipulation is mainly realized using kinesthetic teaching, necessitating expensive and specialized hardware as well as an expert.
The joint research project Motor Dreaming between IAS and HRI takes a different perspective on the problem. It targets to combine data driven learning and exploitative learning in an efficient way. As an alternative to building a skill representation exclusively from data, the core idea is to make additional use of generative models that allow an internal simulation of the task, also known as mental rehearsal. This step involves devising physical simulation models from the real situation, and being able to play the through in different variations. Such a mental rehearsal allows to incorporate uncertainty, such aiming to increase the robustness of reproduction by learning solutions that can deal with large parameter variations.
Robotics, Physics Simulations, Machine Learning, Automatic Control
Pieczona, S. J.; Muratore, F.; Zäh, M. F. (2016). An Approach for Modelling the Structural Dynamics of a Mechanical System based on a Takagi-Sugeno Representation, International Conference on Competitive Manufacturing (COMA).
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