Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in hig...

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Veröffentlicht in:	IEEE/CAA journal of automatica sinica Jg. 4; H. 4; S. 704 - 713
Hauptverfasser:	Safavi, Ali, Zadeh, Mehrdad H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway Chinese Association of Automation (CAA) 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Force Hidden Markov models Human engineering Human motion Human-robot interaction Mathematical models Optimization Performance prediction Predictive control Predictive models Robot learning Robots
ISSN:	2329-9266, 2329-9274
Online-Zugang:	Volltext
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