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Hybrid IDM/Impedance learning in human movements

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Teng KP, Chew CM, Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Burdet, E., Teng KP, Chew CM, Peters, J., & BT (2001). Hybrid IDM/Impedance learning in human movements. Proceedings of the 1st International Symposium on Measurement, Analysis and Modeling of Human Functions (ISHF2001), 1-9.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E210-4
Zusammenfassung
In spite of motor output variability and the delay in the sensori-motor, humans routinely perform intrinsically un- stable tasks. The hybrid IDM/impedance learning con- troller presented in this paper enables skilful performance in strong stable and unstable environments. It consid- ers motor output variability identified from experimen- tal data, and contains two modules concurrently learning the endpoint force and impedance adapted to the envi- ronment. The simulations suggest how humans learn to skillfully perform intrinsically unstable tasks. Testable predictions are proposed.