In recent years, it has been suggested that social robots have potential as tutors and educators for both children and adults. While robots have been shown to be effective in teaching knowledge and skill-based topics, we wish to explore how social robots can be used to tutor a second language to young children. As language learning relies on situated, grounded and social learning, in which interaction and repeated practice are central, social robots hold promise as educational tools for supporting second language learning. This paper surveys the developmental psychology of second language learning and suggests an agenda to study how core concepts of second language learning can be taught by a social robot. It suggests guidelines for designing robot tutors based on observations of second language learning in human-human scenarios, various technical aspects and early studies regarding the effectiveness of social robots as second language tutors.

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Generating spatial referring expressions is key to allowing robots to communicate with people in an environment. The focus of most algorithms for generation is to create a non-ambiguous description, and how best to deal with the combination explosion this can create in a complex environment. However, this is not how people naturally communicate. Humans tend to give an under-specified description and then rely on a strategy of repair to reduce the number of possible locations or objects until the correct one is identified, what we refer to here as a dynamic description. We present here a method for generating these dynamic descriptions for Human Robot Interaction, using machine learning to generate repair statements. We also present a study with 61 participants in a task on object placement. This task was presented in a 2D environment that favored a non-ambiguous description. In this study we demonstrate that our dynamic method of communication can be more efficient for people to identify a location compared to one that is non-ambiguous.

This paper provides a qualitative review of different object recognition techniques relevant for near-proximity Human-Robot Interaction. These techniques are divided into three categories: 2D correspondence, 3D correspondence and nonvision based methods. For each technique an implementation is chosen that is representative of the existing technology to provide a broad review to assist in selecting an appropriate method for tabletop object recognition manipulation. For each of these techniques we give their strengths and weaknesses based on defined criteria. We then discuss and provide recommendations for each of them.

We conducted a study with 25 children to investigate the efectiveness of a robot measuring and encouraging production of spatial concepts in a second language compared to a human experimenter. Productive vocabulary is often not measured in second language learning, due to the diiculty of both learning and assessing productive learning gains. We hypothesized that a robot peer may help assessing productive vocabulary. Previous studies on foreign language learning have found that robots can help to reduce language anxiety, leading to improved results. In our study we found that a robot is able to reach a similar performance to the experimenter in getting children to produce, despite the person's advantages in social ability, and discuss the extent to which a robot may be suitable for this task. CCS CONCEPTS • Human-centered computing → User studies; • Social and professional topics → Assistive technologies; • Computing methodologies → Natural language processing; Cognitive robotics;