Wafa Johal

R2T2: Robotics to integrate educational efforts in South Africa and Europe

This paper presents the first cross-continental collaborative robotic event based around education. It was entitled R2T2 and it involved more than 100 children from Europe and Africa. Based on remo...

Cellulo: Versatile Handheld Robots for Education

In this article, we present Cellulo, a novel robotic platform that investigates the intersection of three ideas for robotics in education: designing the robots to be versatile and generic tools; blending robots into the classroom by designing them to be pervasive objects and by creating tight interactions with (already pervasive) paper; and finally considering the practical constraints of real classrooms at every stage of the design. Our platform results from these considerations and builds on a unique combination of technologies: groups of handheld haptic-enabled robots, tablets and activity sheets printed on regular paper. The robots feature holonomic motion, haptic feedback capability and high accuracy localization through a microdot pattern overlaid on top of the activity sheets, while remaining affordable (robots cost about 125 at the prototype stage) and classroom-friendly. We present the platform and report on our first interaction studies, involving about 230 children.

Permanent magnet-assisted omnidirectional ball drive

We present an omnidirectional ball wheel drive design that utilizes a permanent magnet as the drive roller to generate the contact force. Particularly interesting for novel human-mobile robot interaction scenarios where the users are expected to physically interact with many palm-sized robots, our design combines simplicity, low cost and compactness. We first detail our design and explain its key parameters. Then, we present our implementation and compare it with an omniwheel drive built with identical conditions and similar cost. Finally, we elaborate on the main advantages and drawbacks of our design.

Windfield: Learning Wind Meteorology with Handheld Haptic Robots

This article presents a learning activity and its user study involving the Cellula platform, a novel versatile robotic tool designed for education. In order to show the potential of Cel-lulo in the classroom as part of standard curricular activities, we designed a learning activity called Windfield that aims to teach the atmospheric formation mechanism of wind to early middle school children. The activity involves a didactic sequence, introducing the Cellulo robots as hot air balloons and enabling children to feel the wind force through haptic feedback. We present a user study, designed in the form of a real hour-long lesson, conducted with 24 children in 8 groups who had no prior knowledge in the subject. Collaborative metrics within groups and individual performances about the learning of key concepts were measured with only the hardware and software integrated in the platform in a completely automated manner. The results show that almost all participants showed learning of symmetric aspects of wind formation while about half showed learning of asymmetric vectorial aspects that are more complex.

Haptic-Enabled Handheld Mobile Robots: Design and Analysis

The Cellulo robots are small tangible robots that are designed to represent virtual interactive point-like objects that reside on a plane within carefully designed learning activities. In the context of these activities, our robots not only display autonomous motion and act as tangible interfaces, but are also usable as haptic devices in order to exploit, for instance, kinesthetic learning. In this article, we present the design and analysis of the haptic interaction module of the Cellulo robots. We first detail our hardware and controller design that is low-cost and versatile. Then, we describe the task-based experimental procedure to evaluate the robots haptic abilities. We show that our robot is usable in most of the tested tasks and extract perceptive and manipulative guidelines for the design of haptic elements to be integrated in future learning activities. We conclude with limitations of the system and future work.

When deictic gestures in a robot can harm child-robot collaboration

This paper describes research aimed at supporting childrens reading practices using a robot designed to interact with children as their reading companion. We use a learning by teaching scenario in which the robot has a similar or lower reading level compared to children, and needs help and extra practice to develop its reading skills. The interaction is structured with robot reading to the child and sometimes making mistakes as the robot is considered to be in the learning phase. Child corrects the robot by giving it instant feedbacks. To understand what kind of behavior can be more constructive to the interaction especially in helping the child, we evaluated the effect of a deictic gesture, namely pointing on the childs ability to find reading mistakes made by the robot. We designed three types of mistakes corresponding to different levels of reading mastery. We tested our system in a within-subject experiment with 16 children. We split children into a high and low reading proficiency even-though they were all beginners. For the high reading proficiency group, we observed that pointing gestures were beneficial for recognizing some types of mistakes that the robot made. For the earlier stage group of readers pointing were helping to find mistakes that were raised upon a mismatch between text and illustrations. However, surprisingly, for this same group of children, the deictic gestures were disturbing in recognizing mismatches between text and meaning.

Iterative Design of an Upper Limb Rehabilitation Game with Tangible Robots

Rehabilitation aims to ameliorate deficits in motor control via intensive practice with the affected limb. Current strategies, such as one-on-one therapy done in rehabilitation centers, have limitations such as treatment frequency and intensity, cost and requirement of mobility. Thus, a promising strategy is home-based therapy that includes task specific exercises. However, traditional rehabilitation tasks may frustrate the patient due to their repetitive nature and may result in lack of motivation and poor rehabilitation. In this article, we propose the design and verification of an effective upper extremity rehabilitation game with a tangible robotic platform named Cellulo as a novel solution to these issues. We first describe the process of determining the design rationales to tune speed, accuracy and challenge. Then we detail our iterative participatory design process and test sessions conducted with the help of stroke, brachial plexus and cerebral palsy patients (18 in total) and 7 therapists in 4 different therapy centers. We present the initial quantitative results, which support several aspects of our design rationales and conclude with our future study plans.

Keep on moving! Exploring anthropomorphic effects of motion during idle moments

In this paper, we explored the effect of a robots subconscious gestures made during moments when idle (also called adaptor gestures) on anthropomorphic perceptions of five year old children. We developed and sorted a set of adaptor motions based on their intensity. We designed an experiment involving 20 children, in which they played a memory game with two robots. During moments of idleness, the first robot showed adaptor movements, while the second robot moved its head following basic face tracking. Results showed that the children perceived the robot displaying adaptor movements to be more human and friendly. Moreover, these traits were found to be proportional to the intensity of the adaptor movements. For the range of intensities tested, it was also found that adaptor movements were not disruptive towards the task. These findings corroborate the fact that adaptor movements improve the affective aspect of child-robot interactions (CRI) and do not interfere with the childs performances in the task, making them suitable for CRI in educational contexts.

Workshop on Robots for Learning: R4L

While robots have been popular as a tool for STEM teaching, the use of robots in other learning scenarios is novel. The field of HRI has started to report on how to make effective robots usable in educational contexts. However, many challenges remain. For instance, which interaction strategies aid learning, and which hamper learning? How can we deal with the current technical limitations of robots? Answering these and other questions requires a multidisciplinary effort, including contributions from pedagogy, developmental psychology, (computational) linguistics, artificial intelligence and HRI, among others. This abstract provides a brief overview of the current state-of-the-art in social robots designed for learning and describes the aims of the Robots for Learning (R4L) workshop in bringing together a multidisciplinary audience for furthering the development of market-ready educational robots.

Automated human-level diagnosis of dysgraphia using a consumer tablet

The academic and behavioral progress of children is associated with the timely development of reading and writing skills. Dysgraphia, characterized as a handwriting learning disability, is usually associated with dyslexia, developmental coordination disorder (dyspraxia), or attention deficit disorder, which are all neuro-developmental disorders. Dysgraphia can seriously impair children in their everyday life and require therapeutic care. Early detection of handwriting difficulties is, therefore, of great importance in pediatrics. Since the beginning of the 20th century, numerous handwriting scales have been developed to assess the quality of handwriting. However, these tests usually involve an expert investigating visually sentences written by a subject on paper, and, therefore, they are subjective, expensive, and scale poorly. Moreover, they ignore potentially important characteristics of motor control such as writing dynamics, pen pressure, or pen tilt. However, with the increasing availability of digital tablets, features to measure these ignored characteristics are now potentially available at scale and very low cost. In this work, we developed a diagnostic tool requiring only a commodity tablet. To this end, we modeled data of 298 children, including 56 with dysgraphia. Children performed the BHK test on a digital tablet covered with a sheet of paper. We extracted 53 handwriting features describing various aspects of handwriting, and used the Random Forest classifier to diagnose dysgraphia. Our method achieved 96.6% sensibility and 99.2% specificity. Given the intra-rater and inter-rater levels of agreement in the BHK test, our technique has comparable accuracy for experts and can be deployed directly as a diagnostics tool.