In this paper we describe our work-in-progress to create an interface that enables users to browse and select data within an Augmented Reality environment, using a virtual cube object that can be interacted with through 3D gestural input. We present the prototype design (including the graphical elements), describe the interaction possibilities of touching the cube with the hand/finger, and put the prototype into the context of our Augmented Reality for Public Engagement (PEAR) framework. An interactive prototype was implemented and runs on a typical off-the-shelf smart-phone device.

This paper presents a prototype that facilitates the work of Speech and Language Therapists (SLTs) by providing an Android mobile device application that allows the therapist to focus on the patient rather than taking notes during exercises. Each physical object used by the therapist in those exercises can be given digital properties using Near Field Communication (NFC) tags. The registration does not require a high level of ICT skills from the therapists. SLTs often use such objects in non-technology driven exercises that deal with classification, seriation and inclusion. The application offers such exercises developed in close collaboration with two SLTs, and our aim was to provide therapists with a way to efficiently record activities while working with a patient using a mobile application. The tool was validated through several expert reviews, a usability study as well as a trial with a patient in Paris, France.

The recently finished EU project JuxtaLearn aimed at supporting students’ learning of STEM subjects through the creation, exchange and discussion of learner-made videos. The approach is based on an eight-stage activity cycle in the beginning of which teachers identify specific “stumbling blocks” for a given theme (or “tricky topic”). In JuxtaLearn, video comments were analyzed to extract information on the learners’ acquisition and understanding of domain concepts, especially to detect problems and misconceptions. These analyses were based on mapping texts to networks of concepts (“network-text analysis”) as a basis for further processing. In this article we use data collected from recent field trials to shed light on what is actually discussed when students share their own videos in science domains. Would the aspect of video-making dominate over activities related to a deepening of domain understanding? Our findings indicate that there are different ways of balancing both aspects and interventions will be needed to bring forth the desired blend.

Recent years have seen a growing interest in supporting learning activities/scenarios that go beyond the traditional classroom context as well as the development of pervasive computing scenarios supported by display installations. In order to explore such interactive scenarios that span video-based learning activities across school contexts, we have developed two web-based functional prototypes of public display applications and performed a field evaluation during an initial test-deployment. The system consists of a public display endpoint providing video content enriched with quizzes related to this content, and a mobile endpoint providing interactivity and user participation. During a three weeks test-deployment at two Swedish schools, the display system was evaluated and important requirements for the next iterations were gathered. This work presents the results of the test-deployment and the users' adoption (usage patterns), and discusses the specialties of introducing such a system into educational environments. The deployment and the corresponding study enabled us to validate in real settings the overall technical approach and to test different perspectives of display usage. The conclusions point to the need to further understand how to promote an integrated view of display utilization in schools.

People like to play, and robotic technology offers the opportunity to interact with artifacts in new ways. Robots co-existing with humans in domestic and public environments are expected to behave as companions, also engaging in playful interaction. If a robot is small, we foresee that people will want to be able to pick it up and express their intentions playfully by hugging, shaking and moving it around in various ways. Such robots will need to recognize these gestures--which we call “full-body gestures” because they affect the robot's full body. Inertial sensors inside the robot could be used to detect these gestures, in order to avoid having to rely on external sensors in the environment. However, it is not obvious which gestures typically occur during play, and which of these can be reliably detected. We therefore investigate full-body gesture recognition using Sponge Robot, a small humanoid robot equipped with inertial sensors and designed for playful human-robot interaction.

This paper presents a follow-up to a previous user-study that considered the participants' acknowledgment of feedback from a robot, to which they had to demonstrate the task of laying a table. The question was if (and how) they modified their teaching instructions when the robot stated a misunderstanding, and whether they remained consistent for the rest of the sub-tasks.

In this paper we describe three lines of research related to the issue of helping robots imitate people. These studies are based on observed human behaviour, technical metrics and implemented technical solutions. The three lines of research are: (a) a number of user studies that show how humans naturally tend to demonstrate a task for a robot to learn, (b) a formal approach to tackle the problem of what a robot should imitate, and (c) a technology-driven conceptual framework and technique, inspired by social learning theories, that addresses how a robot can be taught. In this merging exercise we will try to propose a way through this problem space, towards the design of a Human-Robot Interaction (HRI) system able to be taught by humans via demonstration.

In this paper, we explore some conceptual issues, relevant for the design of robotic systems aimed at interacting with humans in domestic environments. More specifically, we study the role of the robot's feedback (positive or negative acknowledgment of understanding) on a human teacher's demonstration of a routine home task (laying a table). Both the human and the system's perspectives are considered in the analysis and discussion of results from a human-robot user study, highlighting some important conceptual and practical issues. These include the lack of explicitness and consistency on people's demonstration strategies. Furthermore, we discuss the need to investigate design strategies to elicit people's knowledge about the task and also successfully advertize the robot's abilities in order to promote people's ability to provide appropriate demonstrations.

Imitation is a powerful mechanism that allows agents to learn via their interactions within a social context. An artificial system that is capable of exploiting this imitative learning capability would be able to acquire new skills and tasks from interaction with another agent (typically a human or another robot). Imitative social learning therefore presents a very interesting paradigm in robotics and computer science and within this paradigm robotics researchers are heavily influenced from interdisciplinary studies typically in biology, ethology and psychology. This tutorial takes such an interdisciplinary approach and aims to present the background and theories of imitation from biology, ethology and psychology together with some of their practical implementations in robotics. The aim of tutorial is to disseminate this research field to a wider audience.

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment [degrees of freedom (DOFs), body morphology, constraints, affordances, and so on]. Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one, and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behavior can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated and validated in a number of simulated 3-D robotic examples, using agents described by simple kinematic models and different types of correspondence mappings.

Imitation is a powerful learning tool when humans and robots interact in a social context. A series of experimental runs and a small pilot user study were conducted to evaluate the performance of a system designed for robot imitation. Performance assessments of similarity of imitative behaviours were carried out by machines and by humans: the system was evaluated quantitatively (from a machine-centric perspective) and qualitatively (from a human perspective) in order to study the reconciliation of these views. The experimental results presented here illustrate how the number of exceptions can be used as a performance measure by a robotic or software imitator of an object manipulation behaviour. (In this context, exceptions are events when the optimal displacement and/or rotation that minimize the dissimilarity metrics used to generate a corresponding imitative behaviour cannot be directly achieved in the particular context.) Results of the user study giving similarity judgments on imitative behaviours were used to examine how the quantitative measure of the number of exceptions (from a robot's perspective) corresponds to the qualitative evaluation of similarity (from a human's perspective) for the imitative behaviours generated by the jabberwocky system. Results suggest that there is a good alignment between this quantitive system centered assessment and the more qualitative human-centered assessment of imitative performance.

This paper addresses the problem of body mapping in robotic imitation where the demonstrator and imitator may not share the same embodiment (degrees of freedom (DOFs), body morphology, constraints, affordances and so on). Body mappings are formalized using a unified (linear) approach via correspondence matrices, which allow one to capture partial, mirror symmetric, one-to-one, one-to-many, many-to-one and many-to-many associations between various DOFs across dissimilar embodiments. We show how metrics for matching state and action aspects of behaviour can be mathematically determined by such correspondence mappings, which may serve to guide a robotic imitator. The approach is illustrated in a number of examples, using agents described by simple kinematic models and different types of correspondence mappings. Also, focusing on aspects of displacement and orientation of manipulated objects, a selection of metrics are presented, towards a characterization of the space of effect metrics

When determining subject preferences for human-robot interaction, an important issue is the interpretation of the subjects' responses during the trials. Employing a non-intrusive approach, this paper discusses the methodological issues for annotating vision data by allowing the subjects to indicate their comfort using a handheld comfort level device during the trials. In previous research, the analysis of collected comfort and vision data was made difficult due to problems concerning the manual synchronization of different modalities. In the current paper, we overcome this issue by real-time integration of the subject's feedback on subjective comfort into the video stream. The implications for more efficient analysis of human-robot interaction data, as well as possible future developments of this approach are discussed

Imitation is a powerful mechanism whereby knowledge may be transferred between agents (both biological and artificial). Key problems on the topic of imitation have emerged in various areas close to artificial intelligence, including the cognitive and social sciences, animal behavior, robotics, human-computer interaction, embodied intelligence, software engineering, programming by example and machine learning. Artificial systems used to study imitation can both test models of imitation derived from observational or neurobiological data on imitation in animals and then apply them to different kinds of nonbiological systems ranging from robots to software agents. A crucial problem in imitation is the correspondence problem, mapping action sequences of the demonstrator and the imitator agent. This problem becomes particularly obvious when the two agents do not share the same embodiment and affordances. This paper describes a new general imitation mechanism called ALICE (action learning for imitation via correspondence between embodiments) that specifically addresses the correspondence problem. The mechanism is implemented and its efficacy illustrated on the "chessworld" testbed that was created to study imitation from an agent-based perspective, i.e., by a particular agent in a particular environment.