Matej Hoffmann: Biologically inspired robot body models and self-calibration
Typically, mechanical design specifications provide the basis for a robot model and kinematic and dynamic mappings are constructed and remain fixed during operation. However, there are many sources of inaccuracies (e.g., assembly process, mechanical elasticity, friction). Furthermore, with the advent of collaborative, social, or soft robots, the stiffness of the materials and the precision of the manufactured parts drops and CAD models provide a less accurate basis for the models. Humans, on the other hand, seamlessly control their complex bodies, adapt to growth or failures, and use tools. Exploiting multimodal sensory information plays a key part in these processes. In this talk, I will establish differences between body representations in the brain and robot body models and assess the possibilities for learning robot models in biologically inspired ways.
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BIO
Matej Hoffmann completed the PhD degree and then served as Senior Research Associate at the Artificial Intelligence Laboratory, University of Zurich, Switzerland (Prof. Rolf Pfeifer, 2006–2013). In 2013 he joined the iCub Facility of the Italian Institute of Technology (Prof. Giorgio Metta), supported by a Marie Curie Intra-European Fellowship. In 2017, he joined the Department of Cybernetics, FEE, CTU, where he is currently serving as an Assistant Professor. His research interests are in humanoid, cognitive developmental, and collaborative robotics.
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