로봇의 고개를 움직이는 동작과 타이밍이 인간과 로봇의 상호작용에 미치는 효과

학위논문(석사) -- 서울대학교대학원 : 인문대학 협동과정 인지과학전공, 2023. 2. Sowon Hahn.In recent years, robots with artificial intelligence capabilities have become ubiquitous in our daily lives. As intelligent robots are interacting closely with humans, social abilities of robots are increasingly more important. In particular, nonverbal communication can enhance the efficient social interaction between human users and robots, but there are limitations of behavior expression. In this study, we investigated how minimal head movements of the robot influence human-robot interaction. We newly designed a robot which has a simple shaped body and minimal head movement mechanism. We conducted an experiment to examine participants' perception of robots different head movements and timing. Participants were randomly assigned to one of three movement conditions, head nodding (A), head shaking (B) and head tilting (C). Each movement condition included two timing variables, prior head movement of utterance and simultaneous head movement with utterance. For all head movement conditions, participants' perception of anthropomorphism, animacy, likeability and intelligence were higher compared to non-movement (utterance only) condition. In terms of timing, when the robot performed head movement prior to utterance, perceived naturalness was rated higher than simultaneous head movement with utterance. The findings demonstrated that head movements of the robot positively affects user perception of the robot, and head movement prior to utterance can make human-robot conversation more natural. By implementation of head movement and movement timing, simple shaped robots can have better social interaction with humans.최근 인공지능 로봇은 일상에서 흔하게 접할 수 있는 것이 되었다. 인간과의 교류가 늘어남에 따라 로봇의 사회적 능력은 더 중요해지고 있다. 인간과 로봇의 사회적 상호작용은 비언어적 커뮤니케이션을 통해 강화될 수 있다. 그러나 로봇은 비언어적 제스처의 표현에 제약을 갖는다. 또한 로봇의 응답 지연 문제는 인간이 불편한 침묵의 순간을 경험하게 한다. 본 연구를 통해 로봇의 고개 움직임이 인간과 로봇의 상호작용에 어떤 영향을 미치는지 알아보았다. 로봇의 고개 움직임을 탐구하기 위해 단순한 형상과 고개를 움직이는 구조를 가진 로봇을 새롭게 디자인하였다. 이 로봇을 활용하여 로봇의 머리 움직임과 타이밍이 참여자에게 어떻게 지각되는지 실험하였다. 참여자들은 3가지 움직임 조건인, 끄덕임 (A), 좌우로 저음 (B), 기울임 (C) 중 한 가지 조건에 무작위로 선정되었다. 각각의 고개 움직임 조건은 두 가지 타이밍(음성보다 앞선 고개 움직임, 음성과 동시에 일어나는 고개 움직임)의 변수를 갖는다. 모든 타입의 고개 움직임에서 움직임이 없는 조건과 비교하여 로봇의 인격화, 활동성, 호감도, 감지된 지능이 향상된 것을 관찰하였다. 타이밍은 로봇의 음성보다 고개 움직임이 앞설 때 자연스러움이 높게 지각되는 것으로 관찰되었다. 결과적으로, 로봇의 고개 움직임은 사용자의 지각에 긍정적인 영향을 주며, 앞선 타이밍의 고개 움직임이 자연스러움을 향상시키는 것을 확인하였다. 고개를 움직이는 동작과 타이밍을 통해 단순한 형상의 로봇과 인간의 상호작용이 향상될 수 있음을 본 연구를 통해 확인하였다.Chapter 1. Introduction 1 1.1. Motivation 1 1.2. Literature Review and Hypotheses 3 1.3. Purpose of Study 11 Chapter 2. Experiment 13 2.1. Methods 13 2.2. Results 22 2.3. Discussion 33 Chapter 3. Conclusion 35 Chapter 4. General Discussion 37 4.1. Theoretical Implications 37 4.2. Practical Implications 38 4.3. Limitations and Future work 39 References 41 Appendix 53 Abstract in Korean 55석

Novelty detection system for a social robot

This Thesis presents a system that allows a social human-interactive robot to be able to actively learn novel stimuli presented to it, expanding its knowledge base. The system detects unknown patterns and decides when those patterns are worth being learned by the robot. The system architecture leans on novelty detection algorithms, that serve to implement two steps: rst ltering noise entries; and then evaluating if the entries that have passed the noise lter are known by the existing model, or on the contrary, they are novel entries that are worth learning. When a novel entry is identi ed, the system activates the learning process to update the model with this new data. The novelty detection system is evaluated in the pose learning domain. The dataset is composed by 28 users that teach 3 di erent poses to the system. In the experiments, we compare the performance of four di erent novelty detection algorithms for this task. We rst evaluate the noise lter by analyzing how many entries from the same pose have to be shown to the robot to pass the lter. The second step is tested training the system with one of the poses, then evaluating if the algorithms are able to detect test entries from other poses as novel. A third experiment tests our system for detecting in-class novelties. The results show that the performances vary between the novelty detection algorithms. The best performance is achieved by GMM, with a 86% F score for detecting new poses and a 80% F score to detect variations within poses. This novelty detection system opens the door for robotic systems to be able to act as active learners, making their own decisions about when it is worth to learn from new stimuli. Additionally, to the extent of our knowledge, there is no reference on Novelty Detection for pose recognition in a Human-Robot interactive application, so this work is a novelty itself.Ingeniería Electrónica Industrial y Automátic

Desarrollo de terapias de rehabilitación motora teleoperadas con el robot NAO

This bachelor thesis presents two components that recognize the body of the patient through a 3D sensor and give to the therapist and the medical group an interface to increase the knowledge of the sys-tem database by adding new poses and exercises for the planning component. It is part of a project called NAOTherapist whose objective is to supervise upper-limb rehabilitation sessions for kids with Obstetric Brachial Plexus Palsy and Cerebral Palsy. This project was developed to help the therapists with their sessions which require continuous attention from the patients, and so that it would also let them treat more patients. If a robot plans the therapy session, checks how the patient is doing and corrects him, the rehabilitation session can be managed autonomously with the robot alone. This bachelor thesis consists on recognizing the body of the patient though the 3D sensor creating a complete model of the body, showing a skeleton visor, and facilitates the capture of new poses and ex-ercises to improve the basic knowledge of the robot so that it performs better in the therapeutic sessions.Este trabajo está compuesto por dos componentes, uno que reconoce el cuerpo del paciente a través de un sensor 3D y otro que proporciona al terapeuta y/o médico rehabilitador una interfaz para añadir nuevas poses y ejercicios a la base de conocimiento del sistema. Forma parte del proyecto NAOT-herapist, cuyo objetivo es la supervisión de sesiones de rehabilitación de los miembros superiores para niños con Parálisis Braquial Obstétrica y Parálisis Cerebral. Es un proyecto que nació para ayudar a los terapeutas en sus sesiones que requerían mucha atención y así que éste tenga más tiempo para otros pa-cientes. Si un robot realiza la planificación de la sesión terapéutica, comprueba como el paciente realiza la sesión y lo corrige, proporciona a los terapeutas más tiempo para otros pacientes, y al mismo tiempo la terapia robótica sirve para que los niños se sientan mucho más motivados en sus sesiones de rehabilita-ción. Este proyecto fin de grado reconoce el cuerpo del paciente a través de un sensor 3D, crea un mo-delo completo del cuerpo humano, muestra un visor del esqueleto y permite la captura de las nuevas poses y ejercicios para mejorar la base de conocimiento del robot y que éste produzca mejores sesiones terapéuticas.Grado en Ingeniería Informátic

Teaching Human Poses Interactively to a Social Robot

The main activity of social robots is to interact with people. In order to do that, the robot must be able to understand what the user is saying or doing. Typically, this capability consists of pre-programmed behaviors or is acquired through controlled learning processes, which are executed before the social interaction begins. This paper presents a software architecture that enables a robot to learn poses in a similar way as people do. That is, hearing its teacher’s explanations and acquiring new knowledge in real time. The architecture leans on two main components: an RGB-D (Red-, Green-, Blue- Depth) -based visual system, which gathers the user examples, and an Automatic Speech Recognition (ASR) system, which processes the speech describing those examples. The robot is able to naturally learn the poses the teacher is showing to it by maintaining a natural interaction with the teacher. We evaluate our system with 24 users who teach the robot a predetermined set of poses. The experimental results show that, with a few training examples, the system reaches high accuracy and robustness. This method shows how to combine data from the visual and auditory systems for the acquisition of new knowledge in a natural manner. Such a natural way of training enables robots to learn from users, even if they are not experts in robotics

Adaptación de una plataforma robótica social e interactiva para su uso en rehabilitación bimanual intensiva

La plataforma NaoTherapist es un sistema robótico cuyo objetivo es desarrollar sesiones de rehabilitación socialmente interactivas para pacientes pediátricos con impedimentos físicos. Este proyecto se inició como una herramienta de apoyo en las terapias de rehabilitación con niñoos, pues estas requieren una mayor atenciónn continua por parte de los terapeutas y una mayor necesidad de motivación, enmascarando los ejercicios de rehabilitación en un entorno de juego. Aunque esta herramienta terapéutica ya fue evaluada con los pacientes reales en una evaluación a largo plazo, se planteó que el sistema participara en un campo de Terapia Intensiva para pacientes con Parálisis Cerebral. Algo que finalmente ha tenido lugar en julio de 2017. Todo esto presenta nuevos retos en el proyecto NaoTherapist, que deben ser considerados para proporcionar una mejor experiencia diaria a los participantes involucrados, de acuerdo a la metodología HABIT empleada en dicho campamento. Este trabajo describe todo el proceso de desarrollo seguido para la elaboración de nuevos componentes que buscan perfeccionar el modelo de rehabilitación robótica, tanto con la inclusión de nuevos juegos terapéuticos con NAO como con la adaptación individualizada a los usuarios, que permitan mejorar la interacción con los usuarios, proporcionando así una mayor motivación y adherencia en su tratamiento. También se ha trabajado en la actualización de algunos componentes ya desarrollados como el módulo de reconocimiento gestual y monitorización de los pacientes, el cual requería un cambio en pos de ser compatible con la nueva versión del sensor Kinect. Durante este documento se exponen las diversas etapas de desarrollo por las que se ha pasado: planteamiento del problema y búsqueda de documentación, análisis y captura de requisitos mediante entrevistas con terapeutas expertos, diseño e implementación de los componentes, y fase de pruebas. A toda esta exposición se unen unos capítulos especialmente dedicados a la posible puesta en marcha del proyecto como negocio empresarial, y al marco legislativo aplicable a un proyecto de estas características. Cabe destacar que todo el trabajo realizado culmina con una evaluación de la plataforma en un entorno clínico real, con pacientes reales, y una publicación de conferencia.The NaoTherapist platform is a robotic system whose goal is to develop socially interactive rehabilitation sessions for pediatric patients with physical disabilities. This project started as a support tool in rehabilitation therapies with children, since sessions require a greater continuous attention on the part of the therapists and a greater need of motivation, masking the rehabilitation exercises in a game environment. Although this therapeutic tool was already evaluated with the real patients in a long-term evaluation, it was suggested that the system participates in an Intensive Therapy eld for patients with Cerebral Palsy. Something that nally took place in July 2017. All this presents new challenges in the NaoTherapist project, which should be considered to provide a better daily experience for the participants involved, according to the HABIT methodology used in the camp. This document describes the entire development process followed for the elaboration of new components that seek to perfect the robotic rehabilitation model, both with the inclusion of new therapeutic games with NAO and with the individualized adaptation to the users, that allow to improve the interaction with the users, thus providing greater motivation and adherence in their treatment. Theres is also work on the update of some components already developed, as the module of gestural recognition and monitoring of patients, which require a change in order to be compatible with the new version of the Kinect sensor. This document outline the various stages of development that have taken place: problem solving and document search, analysis and capture of requirements through interviews with expert therapists, design and implementation of the components, and testing phase. Throughout this exhibition are joined chapters especially dedicated to the possible implementation of the project as a business, and to the legislative framework applicable to such a project. It should be noted that all the work completed culminates with an evaluation of the platform in a real clinical environment, with real patients, and a conference publication.Grado en Informátic