GlowBots: Robots that Evolve Relationships

GlowBots are small wheeled robots that develop complex relationships between each other and with their owner. They develop attractive patterns which are affected both by user interaction and communication between the robots. The project shows how robots can interact with humans in subtle and sustainable ways for entertainment and enjoyment

Child-Robot Interactions for Second Language Tutoring to Preschool Children

In this digital age social robots will increasingly be used for educational purposes, such as second language tutoring. In this perspective article, we propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and we discuss some technical challenges for developing these. The features we propose include choices to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. We also believe that the first impressions children have about robots are crucial for them to build trust and common ground, which would support child-robot interactions in the long term. We therefore propose a strategy to introduce the robot in a safe way to toddlers. Other features relate to the ability to adapt to individual children's language proficiency, respond contingently, both temporally and semantically, establish joint attention, use meaningful gestures, provide effective feedback and monitor children's learning progress. Technical challenges we observe include automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology humans have. We briefly discuss an experiment in which we investigate how children respond to different forms of feedback the robot can give

Child-Robot Interactions for Second Language Tutoring to Preschool Children

In this digital age social robots will increasingly be used for educational purposes, such as second language tutoring. In this perspective article, we propose a number of design features to develop a child-friendly social robot that can effectively support children in second language learning, and we discuss some technical challenges for developing these. The features we propose include choices to develop the robot such that it can act as a peer to motivate the child during second language learning and build trust at the same time, while still being more knowledgeable than the child and scaffolding that knowledge in adult-like manner. We also believe that the first impressions children have about robots are crucial for them to build trust and common ground, which would support child-robot interactions in the long term. We therefore propose a strategy to introduce the robot in a safe way to toddlers. Other features relate to the ability to adapt to individual children's language proficiency, respond contingently, both temporally and semantically, establish joint attention, use meaningful gestures, provide effective feedback and monitor children's learning progress. Technical challenges we observe include automatic speech recognition (ASR) for children, reliable object recognition to facilitate semantic contingency and establishing joint attention, and developing human-like gestures with a robot that does not have the same morphology humans have. We briefly discuss an experiment in which we investigate how children respond to different forms of feedback the robot can give

Developing Emotional Security Among Children Who Have Been Adopted

This study investigated the development of emotional security among 6-10 year old children who have been adopted by exposing them to an experimental condition during which they could engage with either a live dog or a robotic dog. The live dog was a certified therapy dog; the robotic dog was a FurReal® toy marketed by Hasbro as Biscuit. Utilizing a mixed-method embedded experimental design, the experimental condition was intentionally structured to promote engagement between the participant and the dog or robot. 43 children who had been adopted from the child welfare system were randomly assigned to one of two groups. One group was exposed to a therapy dog (n=22), while another was exposed to the social robotic dog (n=21). The development of emotional security was targeted for measurement in this study using the Reading the Mind in the Eyes Test, a test of social understanding that has been linked in the literature to oxytocin- a hormone premised to be a marker of the development of emotional security. Physiological anxiety was also measured as an indicator of emotional security using the Revised Child Manifest Anxiety Scale-2 (RCMAS-2). Both measures were administered before and after exposure to the experimental condition. A linear mixed-effect regression analysis showed that for boys only, there was a significant effect of engagement with either companion on social understanding (

Essays on Financial Applications of Nonlinear Models

In this thesis, we examine the relationship between news and the stock market. Further, we explore methods and build new nonlinear models for forecasting stock price movement and portfolio optimization based on past stock prices and on one type of big data, news items, which are obtained through the RavenPack News Analytics Global Equities editions. The thesis consists of three essays. In Essay 1, we investigate the relationship between news items and stock prices using the artificial neural network (ANN) model. First, we use Granger causality to ascertain how news items affect stock prices. The results show that news volume is not the Granger cause of stock price change; rather, news sentiment is. Second, we test the semi–strong form efficient market hypothesis, whereas most existing research testing efficient market hypothesis focuses on the weak–form version. Our ANN strategies consistently outperform the passive buy–and–hold strategy and this finding is apparently at odds with the notion of the efficient market hypothesis. Finally, using news sentiment analytics from RavenPack Dow Jones News Analytics, we show positive profitability with out–of–sample prediction using the proposed ANN strategies for Google Inc. (NASDAQ: GOOG). In Essay 2, we expand the utility of the information from news volume and news sentiments to encompass portfolio diversification. For the Dow Jones Industrial Average (DJIA) components, we assign different weights to build portfolios according to their weekly news volumes or news sentiments. Our results show that news volume contributes to portfolio variance both in–sample and out–of–sample: positive news sentiment contributes to the portfolio return in–sample, while negative contributes to the portfolio return out–of–sample, which is a consequence of investors overreacting to the news sentiment. Further, we propose a novel approach to portfolio diversification using the k–Nearest Neighbors (kNN) algorithm based on the idea that news sentiment correlates with stock returns. Out–of–sample results indicate that such strategy dominates the benchmark DJIA index portfolio. In Essay 3, we propose a new model called the Combined Markov and Hidden Markov Model (CMHMM), in which observation is affected by a Markov model and an HMM (Hidden Markov Model) model. The three fundamental questions of the CMHMM are discussed. Further, the application of the CMHMM, in which the news sentiment is one observation and the stock return is the other, is discussed. The empirical results of the trading strategy based on the CMHMM show the potential applications of the proposed model in finance. This thesis contributes to the literature in a number of ways. First, it extends the literature on financial applications of nonlinear models. We explore the applications of the ANNs and kNN in the financial market. Besides, the proposed new CMHMM model adheres to the nature of the stock market and has better potential prediction ability. Second, the empirical results from this dissertation contribute to the understanding of the relationship between news and the stock market. For instance, our research found that news volume contributes to the portfolio return and that investors overreact to news sentiment—a phenomenon that has been discussed by other scholars from different angles

Interaction décisionnelle homme-robot : la planification de tâches au service de la sociabilité du robot

Cette thèse aborde la problématique du robot assistant et plus particulièrement les aspects décisionnels qui y sont liés. Un robot assistant est amené à interargir avec des hommes ce qui impose qu'il doit intègrer dans son processus décisionnel de haut-niveau les contraintes sociales inhérentes à un comportement acceptable par son(ses) partenaire(s) humain(s). Cette thèse propose une approche permettant de décrire de manière générique diverses règles sociales qui sont introduites dans le processus de planification du robot afin d'évaluer la qualité sociale des plans solutions et de ne retenir que le(s) plus approprié(s). Cette thèse décrit également l'implémentation de cette approche sous la forme d'un planificateur de tâches appelé HATP (Human Aware Task Planner en anglais). Enfin, cette thèse propose une validation de l'approche développée grâce à un scénario de simulation et à une mise en oeuvre sur un robot réel.This thesis is about assistive robot challenge et more especially about decisional issues linked to it. An assistive robot has to interact with humans which implies that it must integrate in its high-level decisional process some social constraints inherent in a behaviour acceptable by its human partner(s). This thesis proposes an approach allowing to describe, in a generic way, a set of social rules introduced in the robot planning process in order to evaluate social quality of solution plans and, thus, keep the most appropriate. This thesis also describes implementation of this approach in the form of a task planner called HATP (Human Aware Task Planner). Finally, this thesis proposes a validation of the developed approach with a simulation scenario and an implementation on a real robot

Adaptive Robotics in the Entertainment Industry

In this paper, I discuss the market for adaptive robots in the entertainment industry, and some of the most promising avenues for the future development of this field. A United Nations report forecast an impressive 800% growth of this industry within 2-3 years. However, there are many issues that have to be considered when entering this field/market. Most notably, robotic toy systems can be developed to become either closed or open systems. Here, I promote open systems based on different psychological considerations, and I describe a few systems that we developed to enlighten the possibilities for open systems