Frontiers in Neurorobotics (Oct 2016)
Pragmatic frames for teaching and learning in human-robot interaction: review and challenges
Abstract
One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots, but yet are often used to teach skills flexibly to other humans, and to children in particular. A potential route towards natural and efficient learning and teaching in HRI is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills, and teachers to convey these cues. After defining and discussing the concept of pragmatic frame, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction, and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of them have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery.However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction, and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that 1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; 2) new frames can be learnt continually, building on existing ones and guiding the interaction towards higher levels of complexity and expressivity.To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching.
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