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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Vydáno v:IEEE/CAA journal of automatica sinica Ročník 4; číslo 4; s. 704 - 713
Hlavní autoři: Safavi, Ali, Zadeh, Mehrdad H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway Chinese Association of Automation (CAA) 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users’ performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.
Bibliografie:10-1193/TP
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ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2017.7510634