Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning
By Anna-Lisa Vollmer, Manuel Mühlig, Jochen J. Steil, Karola Pitsch, J. Fritsch, Katharina J. Rohlfing, Britta Wrede. 2014
59 citations recorded by the source corpus.
Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.
Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pierre-Yves Oudeyer
- Adrien Baranes
- Sao Mai Nguyen
- Manuel Lopes
- Olivier Sigaud
- Cédric Colas
- Sébastien Forestier
- Frederic Kaplan
In Control and Systems Engineering
In Social Psychology
Citations in the corpus
- What do we learn about development from baby robots? (2015, background): “Why do some children follow quite different developmental paths? How can we explain the apparent universal tendencies on one hand and the individual variability on the other? Is universality the result of a ‘program’? And when we observe a different developmental path, does this imply that something”