Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Robotics Technology in Mental Health Care

This chapter discusses the existing and future use of robotics and intelligent sensing technology in mental health care. While the use of this technology is nascent in mental health care, it represents a potentially useful tool in the practitioner's toolbox. The goal of this chapter is to provide a brief overview of the field, discuss the recent use of robotics technology in mental health care practice, explore some of the design issues and ethical issues of using robots in this space, and finally to explore the potential of emerging technology

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Developing an Affect-Aware Rear-Projected Robotic Agent

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social capabilities necessary for rich and robust interaction with human beings. To address these problems, this dissertation presents the development of a low-cost, flexible, affect-aware rear-projected robotic agent (called ExpressionBot), that is designed to support verbal and non-verbal communication between the robot and humans, with the goal of closely modeling the dynamics of natural face-to-face communication. The developed robotic platform uses state-of-the-art character animation technologies to create an animated human face (aka avatar) that is capable of showing facial expressions, realistic eye movement, and accurate visual speech, and then project this avatar onto a face-shaped translucent mask. The mask and the projector are then rigged onto a neck mechanism that can move like a human head. Since an animation is projected onto a mask, the robotic face is highly flexible research tool, mechanically simple, and low-cost to design, build and maintain compared with mechatronic and android faces. The results of our comprehensive Human-Robot Interaction (HRI) studies illustrate the benefits and values of the proposed rear-projected robotic platform over a virtual-agent with the same animation displayed on a 2D computer screen. The results indicate that ExpressionBot is well accepted by users, with some advantages in expressing facial expressions more accurately and perceiving mutual eye gaze contact. To improve social capabilities of the robot and create an expressive and empathic social agent (affect-aware) which is capable of interpreting users\u27 emotional facial expressions, we developed a new Deep Neural Networks (DNN) architecture for Facial Expression Recognition (FER). The proposed DNN was initially trained on seven well-known publicly available databases, and obtained significantly better than, or comparable to, traditional convolutional neural networks or other state-of-the-art methods in both accuracy and learning time. Since the performance of the automated FER system highly depends on its training data, and the eventual goal of the proposed robotic platform is to interact with users in an uncontrolled environment, a database of facial expressions in the wild (called AffectNet) was created by querying emotion-related keywords from different search engines. AffectNet contains more than 1M images with faces and 440,000 manually annotated images with facial expressions, valence, and arousal. Two DNNs were trained on AffectNet to classify the facial expression images and predict the value of valence and arousal. Various evaluation metrics show that our deep neural network approaches trained on AffectNet can perform better than conventional machine learning methods and available off-the-shelf FER systems. We then integrated this automated FER system into spoken dialog of our robotic platform to extend and enrich the capabilities of ExpressionBot beyond spoken dialog and create an affect-aware robotic agent that can measure and infer users\u27 affect and cognition. Three social/interaction aspects (task engagement, being empathic, and likability of the robot) are measured in an experiment with the affect-aware robotic agent. The results indicate that users rated our affect-aware agent as empathic and likable as a robot in which user\u27s affect is recognized by a human (WoZ). In summary, this dissertation presents the development and HRI studies of a perceptive, and expressive, conversational, rear-projected, life-like robotic agent (aka ExpressionBot or Ryan) that models natural face-to-face communication between human and emapthic agent. The results of our in-depth human-robot-interaction studies show that this robotic agent can serve as a model for creating the next generation of empathic social robots

Mirroring and recognizing emotions through facial expressions for a Robokind platform

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e ComputadoresFacial expressions play an important role during human social interaction, enabling communicative cues, ascertaining the level of interest or signalling the desire to take a speaking turn. They also give continuous feedback indicating that the information conveyed has been understood. However, certain individuals have difficulties in social interaction in particular verbal and non-verbal communication (e.g. emotions and gestures). Autism Spectrum Disorders (ASD) are a special case of social impairments. Individuals that are affected with ASD are characterized by repetitive patterns of behaviour, restricted activities or interests, and impairments in social communication. The use of robots had already been proven to encourage the promotion of social interaction and skills in children with ASD. Following this trend, in this work a robotic platform is used as a mediator in the social interaction activities with children with special needs. The main purpose of this dissertation is to develop a system capable of automatic detecting emotions through facial expressions and interfacing it with a robotic platform in order to allow social interaction with children with special needs. The proposed experimental setup uses the Intel RealSense 3D camera and the Zeno R50 Robokind robotic platform. This layout has two subsystems, a Mirroring Emotion System (MES) and an Emotion Recognition System (ERS). The first subsystem (MES) is capable of synthetizing human emotions through facial expressions, on-line. The other subsystem (ERS) is able to recognize human emotions through facial features in real time. MES extracts the user facial Action Units (AUs), sends the data to the robot allowing on-line imitation. ERS uses Support Vector Machine (SVM) technique to automatic classify the emotion expressed by the User in real time. Finally, the proposed subsystems, MES and ERS, were evaluated in a laboratorial and controlled environment in order to check the integration and operation of the systems. Then, both subsystems were tested in a school environment in different configurations. The results of these preliminary tests allowed to detect some constraints of the system, as well as validate its adequacy in an intervention setting.As expressões faciais desempenham um papel importante na interação social, permitindo fornecer pistas comunicativas, conhecer o nível de interesse ou sinalizar o desejo de falar. No entanto, algumas pessoas têm dificuldades na interação social, em particular, na comunicação verbal e não-verbal (por exemplo, emoções e gestos). As Perturbações do Espectro do Autismo (PEA) são um caso especial de transtorno e dificuldades sociais. Os indivíduos que são afetados com PEA são caracterizados por padrões repetitivos de comportamento, atividades e interesses restritos e possuem deficiências na comunicação social. A utilização de robôs para incentivar a promoção da interação social e habilidades em crianças com PEA tem sido apresentada na literatura. Seguindo essa tendência, neste trabalho uma plataforma robótica é utilizada como um mediador nas atividades de interação social com crianças com necessidades especiais. O objetivo principal desta dissertação é desenvolver um sistema capaz de detetar automaticamente emoções através de expressões faciais e fazer interface com uma plataforma robótica, a fim de permitir uma interação social com crianças com necessidades especiais. O trabalho experimental proposto utiliza a câmara Intel RealSense 3D e a plataforma robótica Zeno R50 Robokind. Este esquema possui dois subsistemas, um sistema de imitação de expressões faciais (MES) e um sistema de reconhecimentos de emoções (ERS). O primeiro subsistema (MES) é capaz de sintetizar on-line as emoções humanas através de expressões faciais. O subsistema ERS é capaz de reconhecer em tempo-real emoções humanas através de características faciais. O MES extrai as Unidades de Ação faciais do utilizador (UAs), envia os dados para o robô permitindo imitação on-line. O ERS utiliza Support Vector Machine (SVM) para automaticamente classificar a emoção exibida pelo utilizador. Finalmente, os subsistemas propostos, MES e ERS, foram avaliados num ambiente laboratorial e controlado, a fim de verificar a integração e a operação de ambos. Em seguida, os subsistemas foram testados num ambiente escolar em diferentes configurações. Os resultados destes testes preliminares permitiram detetar algumas limitações do sistema, bem como validar a sua adequação na intervenção com crianças com necessidades especiais

Creating robotic characters for long-term interaction

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-181).Researchers studying ways in which humans and robots interact in social settings have a problem: they don't have a robot to use. There is a need for a socially expressive robot that can be deployed outside of a laboratory and support remote operation and data collection. This work aims to fill that need with DragonBot - a platform for social robotics specifically designed for long-term interactions. This thesis is divided into two parts. The first part describes the design and implementation of the hardware, software, and aesthetics of the DragonBot-based characters. Through the use of a mobile phone as the robot's primary computational device, we aim to drive down the hardware cost and increase the availability of robots "in the wild". The second part of this work takes an initial step towards evaluating DragonBot's effectiveness through interactions with children. We describe two different teleoperation interfaces for allowing a human to control DragonBot's behavior differing amounts of autonomy by the robot. A human subject study was conducted and these interfaces were compared through a sticker sharing task between the robot and children aged four to seven. Our results show that when a human operator is able to focus on the social portions of an interaction and the robot is given more autonomy, children treat the character more like a peer. This is indicated by the fact that more children re-engaged the robot with the higher level of autonomy when they were asked to split up stickers between the two participants.by Adam Setapen.S.M

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion