We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.

An increasing number of human-robot interaction (HRI) studies are now taking place in applied settings with children. These interactions often hinge on verbal interaction to effectively achieve their goals. Great advances have been made in adult speech recognition and it is often assumed that these advances will carry over to the HRI domain and to interactions with children. In this paper, we evaluate a number of automatic speech recognition (ASR) engines under a variety of conditions, inspired by real-world social HRI conditions. Using the data collected we demonstrate that there is still much work to be done in ASR for child speech, with interactions relying solely on this modality still out of reach. However, we also make recommendations for childrobot interaction design in order to maximise the capability that does currently exist.

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.

The field of Human-Robot Interaction (HRI) lies at the intersection of several disciplines, and is rightfully perceived as a prime interface between engineering and the social sciences. In particular, our field entertains close ties with social and cognitive psychology, and there are many HRI studies which build upon commonly accepted results from psychology to explore the novel relation between humans and machines. Key to this endeavour is the trust we, as a field, put in the methodologies and results from psychology, and it is exactly this trust that is now being questioned across psychology and, by extension, should be questioned in HRI.The starting point of this paper are a number of failed attempts by the authors to replicate old and established results on social facilitation, which leads us to discuss our arguable over-reliance and over-acceptance of methods and results from psychology. We highlight the recent "replication crisis" in psychology, which directly impacts the HRI community and argue that our field should not shy away from developing its own reference tasks.

In this study, we have designed and built a set of tactile fingertips for integration with a commercial, three-fingered robot hand, the Shadow Modular Grasper. The fingertips are an evolution of an established optical, biomimetic tactile sensor, the TacTip. In developing the tactile fingertips, we have progressed the technology in areas such as miniaturization, development of custom-shaped finger-pads and integration of multiple sensors. From these fingertips, we extract a set of high-level features with intuitive relationships to tactile quantities such as contact location and pressure. We present a simple linear-regression method for predicting roll and pitch of the finger-pad relative to a surface normal and show that the method generalises to unknown depths and shapes. Finally, we apply this prediction to a grasp-control method with the Modular Grasper and show that it can adjust the grasp on three real-world objects from the YCB object set in order to attain a greater area of contact at each fingertip.