The use of robots, as a social stimulus, provides several advantages over using another animal. In particular, for rat-robot studies, robots can produce social behaviour that is reproducible across trials. In the current work, we outline a framework for rat-robot interaction studies, that consists of a novel rat-sized robot (PiRat), models of robotic behavior, and a position tracking system for both robot and rat. We present the design of the framework, including constraints on autonomy, latency, and control. We pilot tested our framework by individually running the robot rat with eight different rats, first through a habituation stage, and then with PiRat performing two different types of behaviour - avoiding and frequently approaching. We evaluate the performance of the framework on latency and autonomy, and on the ability to influence the behaviour of individual rats. We find that the framework performs well on its constraints, engages some of the rats (according to the number of meetings), and features a control scheme that produces reproducible behaviour in rats. These features represent a first demonstration of a closed-loop rat-robot framework.

Motion is a critical aspect of communication, required to create natural interactions between humans and robots. Robots for the classroom pose several constraints on motion, which make them challenging to design, including maintaining the safety of the child and the robot, responding in a timely fashion, and creating motions that are expressive and not scary. In this paper we present the mechanical design of a social robot and demonstrate that it is capable of safe motion within the proximity of children through analysis and empirical testing of the arms. The robot has a novel mechanical design for its two arms, which include torso-mounted, back-drivable, torque-limited stepper motors. The results suggest that our design succeeds at increasing safety levels while enabling the use of socially acceptable speeds of motion during the interaction. This study implies that the design of robotic agents for social interaction with children should consider the design of mechanical features that enable safe contact between the human and the robot while not limiting the robot to slow motions that would impair the timing of the interaction.

Language can be a useful tool for social robots as part of their repertoire of social engagement. This late breaking report outlines preliminary studies into how a child can teach a robot lexicons for colors and color relations. The robot used is a minimal social robot, made from cardboard and foam, that interacts with the children through a simple color naming game. Distributed, non-parametric lexicons similar to those used in previous language learning robot studies are used to store links between words and colors. We visually present the resulting lexicons and highlight the issues that have arisen from this preliminary study and how they can be resolved for future studies. The results of this study indicate that children can teach a social robot lexicons, allowing the children and robot to develop a shared set of symbols for color.

Robots that facilitate touch by children have special requirements in terms of safety and robustness, but little is known about how and when children actually use touch with robots. Tools and techniques are required to sense the variety of children's touch and to interpret the volumes of data generated. This explorative user study investigated children's patterns of touch during game play with a robot. We examined where the children touch the robot and their patterns of touch over time, using a raster-based visualisation of each child's time series of touches, recording patterns of touch across different games and children. We found that children readily engage with the robot, in particular spontaneously touching the robot's hands more than any other area. This user study and the tools developed may aid future designs of robots to autonomously detect when they have been touched.

An important challenge in Human-Robot Interaction (HRI) is the analysis and interpretation of large volumes of data to inform the design process within a complex, multi-faceted research space. In this study, we explore how data visualization techniques can contribute to HRI methodology, particularly in terms of linking qualitative and quantitative analysis methods. Specifically, we present a case study demonstrating the visualization of touch data to identify potential patterns of interaction with a social robot intended for deployment in preschool classrooms.

Pretotyping is a set of techniques, tools, and metrics for gauging the interest in a product, prior to full-scale development [1]. This late breaking report describes a pretotyping case study of an ethnodroid - a robot that functions as an ethnographer - intended to engage with young children and record their learning progress. The central requirement for the project is that the robot will be able to interact socially with children aged 1-6 years in tablet-based tasks. We developed a simple robot made of MDF (thick cardboard), added tablets for the face and torso, and controlled a scripted interaction using Wizard of Oz (WoZ). Children's engagement with the robot was tested in an early learning centre which provided a relatively structured environment (“in the lab”) and at a science fair which provided a relatively unconstrained setting (“in the wild”). The rapid testing revealed distinct effects in the children's attitudes and behaviors in the two user contexts and provided insights into form, sensors and analyses for the design process.

Previous studies have shown how Lingodroids, language learning mobile robots, learn terms for space and time, connecting their personal maps of the world to a publically shared language. One caveat of previous studies was that the robots shared the same cognitive architecture, identical in all respects from sensors to mapping systems. In this paper we investigate the question of how terms for space can be developed between robots that have fundamentally different sensors and spatial representations. In the real world, communication needs to occur between agents that have different embodiment and cognitive capabilities, including different sensors, different representations of the world, and different species (including humans). The novel aspects of these studies is that one robot uses a forward facing camera to estimate appearance and uses a biologically inspired continuous attractor network to generate a topological map; the other robot uses a laser scanner to estimate range and uses a probabilistic filter approach to generate an occupancy grid. The robots hold conversations in different locations to establish a shared language. Despite their different ways of sensing and mapping the world, the robots are able to create coherent lexicons for the space around them.

For humans and robots to communicate using natural language it is necessary for the robots to develop concepts and associated terms that correspond to the human use of words. Time and space are foundational concepts in human language, and to develop a set of words that correspond to human notions of time and space, it is necessary to take into account the way that they are used in natural human conversations, where terms and phrases such as `soon', `in a while', or `near' are often used. We present language learning robots called Lingodroids that can learn and use simple terms for time and space. In previous work, the Lingodroids were able to learn terms for space. In this work we extend their abilities by adding temporal variables which allow them to learn terms for time. The robots build their own maps of the world and interact socially to form a shared lexicon for location and duration terms. The robots successfully use the shared lexicons to communicate places and times to meet again.