Cellulo: Tangible Haptic Swarm Robots for Learning

Robots are steadily becoming one of the significant 21st century learning technologies that aim to improve education within both formal and informal environments. Such robots, called Robots for Learning, have so far been utilized as constructionist tools or social agents that aided learning from distinct perspectives. This thesis presents a novel approach to Robots for Learning that aims to explore new added values by means of investigating uses for robots in educational scenarios beyond those that are commonly tackled: We develop a platform from scratch to be "as versatile as pen and paper", namely as composed of easy to use objects that feel like they belong in the learning ecosystem while being seamlessly usable across many activities that help teach a variety of subjects. Following this analogy, we design our platform as many low-cost, palm-sized tangible robots that operate on printed paper sheets, controlled by readily available mobile computers such as smartphones or tablets. From the learners' perspective, our robots are thus physical and manipulable points of hands-on interaction with learning activities where they play the role of both abstract and concrete objects that are otherwise not easily represented. We realize our novel platform in four incremental phases, each of which consists of a development stage and multiple subsequent validation stages. First, we develop accurately positioned tangibles, characterize their localization performance and test the learners' interaction with our tangibles in a playful activity. Second, we integrate mobility into our tangibles and make them full-blown robots, characterize their locomotion performance and test the emerging notion of moving vs. being moved in a learning activity. Third, we enable haptic feedback capability on our robots, measure their range of usability and test them within a complete lesson that highlights this newly developed affordance. Fourth, we develop the means of building swarms with our haptic-enabled tangible robots and test the final form of our platform in a lesson co-designed with a teacher. Our effort thus contains the participation of more than 370 child learners over the span of these phases, which leads to the initial insights into this novel Robots for Learning avenue. Besides its main contributions to education, this thesis further contributes to a range of research fields related to our technological developments, such as positioning systems, robotic mechanism design, haptic interfaces and swarm robotics.

Synthesis, modeling, and experimental validation of distributed robotic search

James Pugh

The work of this thesis centers around the research subject of distributed robotic search. Within the field of distributed robotic systems, a task of particular interest is attempting to locate one or more targets in a possibly unknown environment. While numerous studies have proposed and analyzed methods to accomplish this, there is currently a lack of a strong foundation of tools and techniques that can be used to facilitate the development and evaluation of different approaches to distributed robotic search. In this work, we aim to provide such a foundation through tools, methods, models, and analysis of experimentation. An often overlooked aspect of the distributed robotic research process is the development and analysis of tools and modules to be used with robotic systems. These may include plug-ins for realistic robotic simulators, software/hardware systems to track multiple mobile robots in real-time, extension boards for robotic platforms, and the robots themselves. Along with other tools developed and used for this work, we focus particularly on the development, characterization, and validation of a fast, accurate on-board system for relative positioning and communication between robots, a capability which is critical for effective distributed robotic search. Designing individual robot controllers to generate a specific group behavior is a difficult and often counter-intuitive process. A possible alternative to hand-crafting distributed search controllers is automatic synthesis using machine-learning techniques. We explore the effectiveness of using a noise-resistant version of the Particle Swarm Optimization algorithm to optimize the weights of an embedded Artificial Neural Network, allowing the robot to learn obstacle avoidance behavior (a common benchmark for robotic learning techniques); we find that this technique appears to offer superior performance as compared to the canonical approach of using Genetic Algorithms for this type of learning. A method for faster learning using distributed evaluation in a robot team is tested and is found to offer comparable performance using only a fraction of the original learning time. This technique can be used for fast, effective learning and adaptation in a distributed robotic system performing search. The process of designing and analyzing algorithms for distributed robotic systems can be greatly facilitated if models are available to describe the dynamics of the algorithm at an abstract level. Inspired by previous examples in the distributed robotic field, we work to design a model of robotic search that captures the system at different levels of abstraction, ranging from accurately recreating the details of individual robots to describing the entire system as an indivisible whole. To capture the entire search process, we model both the exploration phase, where robots cover an environment in an effort to detect traces of targets, and the localization phase, where robots use target emission sensing to navigate towards the target. The utility of our models is demonstrated by using them to develop an effective technique for the declaration phase of search, where robots decide that a target has been accurately localized and announce its position. In distributed robotics research, it is important that techniques developed with abstracted simulations and models are ultimately validated using real robotic platforms in order to verify their correctness. In that spirit, we run systematic sound search experiments using teams of up to ten real robots. These experiments utilize the tools developed throughout the research process, demonstrate the utility of our learning technique for fast search adaptation, and serve to validate our models of distributed robotic localization. In addition, they allow us to analyze and better understand the subtle dynamics of the search process, providing information which should be useful for any future work on distributed robotic search.

EPFL2008

A review: Can robots reshape K-12 STEM education?

Mohammad Ehsanul Karim, Séverin Lemaignan, Francesco Mondada

Can robots in classroom reshape K-12 STEM education, and foster new ways of learning? To sketch an answer, this article reviews, side-by-side, existing literature on robot-based learning activities featuring mathematics and physics (purposefully putting aside the well-studied field of "robots to teach robotics") and existing robot platforms and toolkits suited for classroom environment (in terms of cost, ease of use, orchestration load for the teacher, etc.). Our survey suggests that the use of robots in classroom has indeed moved from purely technology to education, to encompass new didactic fields. We however identified several shortcomings, in terms of robotic platforms and teaching environments, that contribute to the limited presence of robotics in existing curricula; the lack of specific teacher training being likely pivotal. Finally, we propose an educational framework merging the tangibility of robots with the advanced visibility of augmented reality.

IEEE2015

Haptic-Enabled Handheld Mobile Robots: Design and Analysis

Wafa Monia Benkaouar Johal, Pierre Dillenbourg, Francesco Mondada, Ayberk Özgür

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 robot's 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.

ACM2017

Synthesis, modeling, and experimental validation of distributed robotic search

James Pugh

EPFL2008