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 toward natural and efficient learning and teaching in Human-Robot Interaction (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 frames, 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 the works 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 toward 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.
Highlights
Robots have long been predicted to become everyday companions capable to help and assist us in our daily tasks
Some studies on human development recognize social interaction as facilitating learning processes by providing a stable structure (see a summary in Rohlfing et al (2016)). This stable structure is described by the concept of pragmatic frames, which has been introduced by Bruner (1983) and recently been re-introduced by Rohlfing et al (2016)
Leveraging pragmatic frames for robotics might have the potential to overcome the open challenges in robot learning of sensorimotor and linguistic skills
Summary
Robots have long been predicted to become everyday companions capable to help and assist us in our daily tasks. In the example of Bruner’s book-reading frame, which we revisited in the definition of a learning/teaching pragmatic frame, the slot is step (6) in the sequence of behaviors in which the parent utters the correct label of the relevant image (i.e., the learning content). To structure this search space and to cover a diversity of works, we set up a taxonomy based on the categories developed by two reviews of learning approaches, one from an interactional perspective (Thomaz and Breazeal, 2006a) and one from an algorithmic perspective (Cuayáhuitl, 2015) Based on this taxonomy, we selected 15 papers from the robotics literature focusing on scenarios in which the system is learning from a human teacher who teaches the robot new actions or words. The following questions were central to our analyses: What is the structure of interaction? What information is passed? What are the consequences for the learning algorithms?
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