1,605 research outputs found
Towards Dialogue Dimensions for a Robotic Tutor in Collaborative Learning Scenarios
There has been some studies in applying robots to education and recent research on socially intelligent robots show robots as partners that collaborate with people. On the other hand, serious games and interaction technologies have also proved to be important pedagogical tools, enhancing collaboration and interest in the learning process. This paper relates to the collaborative scenario in EMOTE EU FP7 project and its main goal is to develop and present the dialogue dimensions for a robotic tutor in a collaborative learning scenario grounded in human studies. Overall, seven dialogue dimensions between the teacher and students interaction were identified from data collected over 10 sessions of a collaborative serious game. Preliminary results regarding the teachers perspective of the students interaction suggest that student collaboration led to learning during the game. Besides, students seem to have learned a number of concepts as they played the game. We also present the protocol that was followed for the purposes of future data collection in human-human and human-robot interaction in similar scenarios
Producing Acoustic-Prosodic Entrainment in a Robotic Learning Companion to Build Learner Rapport
abstract: With advances in automatic speech recognition, spoken dialogue systems are assuming increasingly social roles. There is a growing need for these systems to be socially responsive, capable of building rapport with users. In human-human interactions, rapport is critical to patient-doctor communication, conflict resolution, educational interactions, and social engagement. Rapport between people promotes successful collaboration, motivation, and task success. Dialogue systems which can build rapport with their user may produce similar effects, personalizing interactions to create better outcomes.
This dissertation focuses on how dialogue systems can build rapport utilizing acoustic-prosodic entrainment. Acoustic-prosodic entrainment occurs when individuals adapt their acoustic-prosodic features of speech, such as tone of voice or loudness, to one another over the course of a conversation. Correlated with liking and task success, a dialogue system which entrains may enhance rapport. Entrainment, however, is very challenging to model. People entrain on different features in many ways and how to design entrainment to build rapport is unclear. The first goal of this dissertation is to explore how acoustic-prosodic entrainment can be modeled to build rapport.
Towards this goal, this work presents a series of studies comparing, evaluating, and iterating on the design of entrainment, motivated and informed by human-human dialogue. These models of entrainment are implemented in the dialogue system of a robotic learning companion. Learning companions are educational agents that engage students socially to increase motivation and facilitate learning. As a learning companionâs ability to be socially responsive increases, so do vital learning outcomes. A second goal of this dissertation is to explore the effects of entrainment on concrete outcomes such as learning in interactions with robotic learning companions.
This dissertation results in contributions both technical and theoretical. Technical contributions include a robust and modular dialogue system capable of producing prosodic entrainment and other socially-responsive behavior. One of the first systems of its kind, the results demonstrate that an entraining, social learning companion can positively build rapport and increase learning. This dissertation provides support for exploring phenomena like entrainment to enhance factors such as rapport and learning and provides a platform with which to explore these phenomena in future work.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Towards a synthetic tutor assistant: The EASEL project and its architecture
Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learnerâs progress, discrimination of the learnerâs utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASELâs unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions
Efficient learning of sequential tasks for collaborative robots: a neurodynamic approach
Dissertação de mestrado integrado em Engenharia Eletrónica, Industrial e ComputadoresIn the recent years, there has been an increasing demand for collaborative robots able to interact and co operate with ordinary people in several human environments, sharing physical space and working closely with
people in joint tasks, both within industrial and domestic environments. In some scenarios, these robots will
come across tasks that cannot be fully designed beforehand, resulting in a need for flexibility and adaptation to
the changing environments.
This dissertation aims to endow robots with the ability to acquire knowledge of sequential tasks using the
Programming by Demonstration (PbD) paradigm. Concretely, it extends the learning models - based on Dynamic
Neural Fields (DNFs) - previously developed in the Mobile and Anthropomorphic Robotics Laboratory (MARLab), at
the University of Minho, to the collaborative robot Sawyer, which is amongst the newest collaborative robots on the
market. The main goal was to endow Sawyer with the ability to learn a sequential task from tutorsâ demonstrations,
through a natural and efficient process.
The developed work can be divided into three main tasks: (1) first, a previously developed neuro-cognitive
control architecture for extracting the sequential structure of a task was implemented and tested in Sawyer,
combined with a Short-Term Memory (STM) mechanism to memorize a sequence in one-shot, aiming to reduce
the number of demonstration trials; (2) second, the previous model was extended to incorporate workspace
information and action selection in a Human-Robot Collaboration (HRC) scenario where robot and human co worker coordinate their actions to construct the structure; and (3) third, the STM mechanism was also extended
to memorize ordinal and temporal aspects of the sequence, demonstrated by tutors with different behavior time
scales.
The models implemented contributed to a more intuitive and practical interaction with the robot for human
co-workers. The STM model made the learning possible from few demonstrations to comply with the requirement
of being an efficient method for learning. Moreover, the recall of the memorized information allowed Sawyer to
evolve from being in a learning position to be in a teaching one, obtaining the capability of assisting inexperienced
co-workers.Nos Ășltimos anos, tem havido uma crescente procura por robĂŽs colaborativos capazes de interagir e cooperar
com pessoas comuns em vĂĄrios ambientes, partilhando espaço fĂsico e trabalhando em conjunto, tanto em
ambientes industriais como domésticos. Em alguns cenårios, estes robÎs serão confrontados com tarefas que
não podem ser previamente planeadas, o que resulta numa necessidade de existir flexibilidade e adaptação ao ambiente que se encontra em constante mudança.
Esta dissertação pretende dotar robÎs com a capacidade de adquirir conhecimento de tarefas sequenciais
utilizando técnicas de Programação por Demonstração. De forma a continuar o trabalho desenvolvido no Laboratório de Robótica Móvel e Antropomórfica da Universidade do Minho, esta dissertação visa estender os modelos
de aprendizagem previamente desenvolvidos ao robÎ colaborativo Sawyer, que é um dos mais recentes no mercado. O principal objetivo foi dotar o robÎ com a capacidade de aprender tarefas sequenciais por demonstração,
através de um processo natural e eficiente.
O trabalho desenvolvido pode ser dividido em trĂȘs tarefas principais: (1) em primeiro lugar, uma arquitetura
de controlo baseada em modelos neurocognitivos, desenvolvida anteriormente, para aprender a estrutura de
uma tarefa sequencial foi implementada e testada no robĂŽ Sawyer, conjugada com um mecanismo de Short Term Memory que permitiu memorizar uma sequĂȘncia apenas com uma demonstração, para reduzir o nĂșmero
de demonstraçÔes necessårias; (2) em segundo lugar, o modelo anterior foi estendido para englobar informação
acerca do espaço de trabalho e seleção de açÔes num cenårio de Colaboração Humano-RobÎ em que ambos
coordenam as suas açÔes para construir a tarefa; (3) em terceiro lugar, o mecanismo de Short-Term Memory foi
tambĂ©m estendido para memorizar informação ordinal e temporal de uma sequĂȘncia de passos demonstrada
por tutores com comportamentos temporais diferentes.
Os modelos implementados contribuĂram para uma interação com o robĂŽ mais intuitiva e prĂĄtica para os
co-workers humanos. O mecanismo de Short-Term Memory permitiu que a aprendizagem fosse realizada a
partir de poucas demonstraçÔes, para cumprir com o requisito de ser um método de aprendizagem eficiente.
Além disso, a informação memorizada permitiu ao Sawyer evoluir de uma posição de aprendizagem para uma
posição em que Ă© capaz de instruir co-workers inexperientes.This work was carried out within the scope of the project âPRODUTECH SIF - SoluçÔes para a IndĂșstria
do Futuroâ, reference POCI-01-0247-FEDER-024541, cofunded by âFundo Europeu de Desenvolvimento Regional (FEDER)â, through âPrograma Operacional Competitividade e Internacionalização (POCI)â
Decoding learning: the proof, promise and potential of digital education
With hundreds of millions of pounds spent on digital technology for education every year â from interactive whiteboards to the rise of oneâtoâone tablet computers â every new technology seems to offer unlimited promise to learning. many sectors have benefitted immensely from harnessing innovative uses of technology. cloud computing, mobile communications and internet applications have changed the way manufacturing, finance, business services, the media and retailers operate.
But key questions remain in education: has the range of technologies helped improve learnersâ experiences and the standards they achieve? or is this investment just languishing as kit in the cupboard? and what more can decision makers, schools, teachers, parents and the technology industry do to ensure the full potential of innovative technology is exploited? There is no doubt that digital technologies have had a profound impact upon the management of learning. institutions can now recruit, register, monitor, and report on students with a new economy, efficiency, and (sometimes) creativity. yet, evidence of digital technologies producing real transformation in learning and teaching remains elusive. The education sector has invested heavily in digital technology; but this investment has not yet resulted in the radical improvements to learning experiences and educational attainment. in 2011, the Review of Education Capital found that maintained schools spent ÂŁ487 million on icT equipment and services in 2009-2010.
1 since then, the education system has entered a state of flux with changes to the curriculum, shifts in funding, and increasing school autonomy. While ring-fenced funding for icT equipment and services has since ceased, a survey of 1,317 schools in July 2012 by the british educational suppliers association found they were assigning an increasing amount of their budget to technology. With greater freedom and enthusiasm towards technology in education, schools and teachers have become more discerning and are beginning to demand more evidence to justify their spending and strategies. This is both a challenge and an opportunity as it puts schools in greater charge of their spending and use of technolog
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