A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback

Vollmer A-L, Hemion NJ (2018)
Frontiers in Robotics and AI 5: 77.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
Enabling users to teach their robots new tasks at home is a major challenge for research in personal robotics. This work presents a user study in which participants were asked to teach the robot Pepper a game of skill. The robot was equipped with a state-of-the-art skill learning method, based on dynamic movement primitives (DMPs). The only feedback participants could give was a discrete rating after each of Pepper's movement executions (“very good,” “good,” “average,” “not so good,” “not good at all”). We compare the learning performance of the robot when applying user-provided feedback with a version of the learning where an objectively determined cost via hand-coded cost function and external tracking system is applied. Our findings suggest that (a) an intuitive graphical user interface for providing discrete feedback can be used for robot learning of complex movement skills when using DMP-based optimization, making the tedious definition of a cost function obsolete; and (b) un-experienced users with no knowledge about the learning algorithm naturally tend to apply a working rating strategy, leading to similar learning performance as when using the objectively determined cost. We discuss insights about difficulties when learning from user provided feedback, and make suggestions how learning continuous movement skills from non-expert humans could be improved.
Erscheinungsjahr
Zeitschriftentitel
Frontiers in Robotics and AI
Band
5
Artikelnummer
77
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Finanzierungs-Informationen
Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Vollmer A-L, Hemion NJ. A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback. Frontiers in Robotics and AI. 2018;5: 77.
Vollmer, A. - L., & Hemion, N. J. (2018). A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback. Frontiers in Robotics and AI, 5, 77. doi:10.3389/frobt.2018.00077
Vollmer, A. - L., and Hemion, N. J. (2018). A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback. Frontiers in Robotics and AI 5:77.
Vollmer, A.-L., & Hemion, N.J., 2018. A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback. Frontiers in Robotics and AI, 5: 77.
A.-L. Vollmer and N.J. Hemion, “A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback”, Frontiers in Robotics and AI, vol. 5, 2018, : 77.
Vollmer, A.-L., Hemion, N.J.: A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback. Frontiers in Robotics and AI. 5, : 77 (2018).
Vollmer, Anna-Lisa, and Hemion, Nikolas J. “A User Study on Robot Skill Learning Without a Cost Function: Optimization of Dynamic Movement Primitives via Naive User Feedback”. Frontiers in Robotics and AI 5 (2018): 77.
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2018-07-10T11:43:50Z