Representation and generalization of bi-manual skills from kinesthetic teaching

The paper presents a modular architecture for bi-manual skill acquisition from kinesthetic teaching. Skills are learned and embedded over several representational levels comprising a compact movement representation by means of movement primitives, a task space description of the bi-manual tool const...

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Bibliographic Details
Published in2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) pp. 560 - 567
Main Authors Reinhart, Rene Felix, Lemme, Andre, Steil, Jochen Jakob
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2012
Subjects
End effectors
Kinematics
Modulation
Robot kinematics
Training
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Summary:The paper presents a modular architecture for bi-manual skill acquisition from kinesthetic teaching. Skills are learned and embedded over several representational levels comprising a compact movement representation by means of movement primitives, a task space description of the bi-manual tool constraint, and the particular redundancy resolution of the inverse kinematics. A comparative evaluation of different architectural configurations identifies a specific modulation scheme for skill execution to achieve optimal spatial generalization from few training samples. Based on this architectural layout together with a novel stabilization approach for dynamical movement primitives, the robust teaching and execution of complex skill sequences is demonstrated on the humanoid robot iCub.
ISSN:2164-0572
DOI:10.1109/HUMANOIDS.2012.6651575