Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks

Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused on the overall prevalent action being executed, but we argue that bimanual human motion cannot always be described suff...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 5; H. 1; S. 187 - 194
Hauptverfasser:	Dreher, Christian R. G., Wachter, Mirko, Asfour, Tamim
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification Classifiers Domains Feature extraction Graphical representations Ground truth Human motion Humanoid Labels Learning from demonstration Machine learning Moving object recognition Object recognition Programming Robots Segmentation semantic scene understanding Semantics Task analysis Three-dimensional displays Video data visual learning
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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