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Tutor Spotter: Proposing a Feature Set and Evaluating It in a Robotic System

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Abstract

From learning by observation, robotic research has moved towards investigations of learning by interaction. This research is inspired by findings from developmental studies on human children and primates pointing to the fact that learning takes place in a social environment. Recently, driven by the idea that learning through observation or imitation is limited because the observed action not always reveals its meaning, scaffolding or bootstrapping processes supporting learning received increased attention. However, in order to take advantage of teaching strategies, a system needs to be sensitive to a tutor as children are. We therefore developed a module allowing for spotting the tutor by monitoring her or his gaze and detecting modifications in object presentation in form of a looming action. In this article, we will present the current state of the development of our contingency detection system as a set of features.

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Authors and Affiliations

  1. CoR-Lab, Applied Informatics Group, Bielefeld University, Bielefeld, Germany

    Katrin S. Lohan, Katharina J. Rohlfing, Karola Pitsch, Joe Saunders, Hagen Lehmann, Chrystopher L. Nehaniv, Kerstin Fischer & Britta Wrede

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  1. Katrin S. Lohan
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  2. Katharina J. Rohlfing
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  3. Karola Pitsch
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  4. Joe Saunders
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  5. Hagen Lehmann
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  6. Chrystopher L. Nehaniv
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  7. Kerstin Fischer
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  8. Britta Wrede
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Correspondence to Katrin S. Lohan.

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Lohan, K.S., Rohlfing, K.J., Pitsch, K. et al. Tutor Spotter: Proposing a Feature Set and Evaluating It in a Robotic System. Int J of Soc Robotics 4, 131–146 (2012). https://doi.org/10.1007/s12369-011-0125-8

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