Vowel priority lip matching scheme and similarity evaluation model based on humanoid robot Ren-Xin

At present, the significance of humanoid robots dramatically increased while this kind of robots rarely enters human life because of its immature development. The lip shape of humanoid robots is crucial in the speech process since it makes humanoid robots look like real humans. Many studies show that vowels are the essential elements of pronunciation in all languages in the world. Based on the traditional research of viseme, we increased the priority of the smooth transition of lip between vowels and propose a lip matching scheme based on vowel priority. Additionally, we also designed a similarity evaluation model based on the Manhattan distance by using computer vision lip features, which quantifies the lip shape similarity between 0-1 provides an effective recommendation of evaluation standard. Surprisingly, this model successfully compensates the disadvantages of lip shape similarity evaluation criteria in this field. We applied this lip-matching scheme to Ren-Xin humanoid robot and performed robot teaching experiments as well as a similarity comparison experiment of 20 sentences with two males and two females and the robot. Notably, all the experiments have achieved excellent results

Design And Development Of A Social Robotic Head - Dorothy

Master'sMASTER OF ENGINEERIN

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

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

ON THE INFLUENCE OF SOCIAL ROBOTS IN COGNITIVE MULTITASKING AND ITS APPLICATION

[Objective] I clarify the impact of social robots on cognitive tasks, such as driving a car or driving an airplane, and show the possibility of industrial applications based on the principles of social robotics. [Approach] I adopted the MATB, a generalized version of the automobile and airplane operation tasks, as cognitive tasks to evaluate participants' performance on reaction speed, tracking performance, and short-term memory tasks that are widely applicable, rather than tasks specific to a particular situation. Also, as the stimuli from social robots, we used the iCub robot, which has been widely used in social communication research. In the analysis of participants, I not only analyzed performance, but also mental workload using skin conductance and emotional analysis of arousal-valence using facial expressions analysis. In the first experiment, I compared a social robot that use social signals with a nonsocial robot that do not use such signals and evaluated whether social robots affect cognitive task performances. In the second experiment, I focused on vitality forms and compared a calm social robot with an assertive social robot. As analysis methods, I adopted Mann-Whitney's U test for one-pair comparisons, and ART-ANOVA for analysis of variance in repeated task comparisons. Based on the results, I aimed to express vitality forms in a robot head, which is smaller in size and more flexible in placement than a full-body humanoid robot, considering car and airplane cockpit's limited space. For that, I developed a novel eyebrow and I decided to use a wire-driven technique, which is widely used in surgical robots to control soft materials. [Main results] In cognitive tasks such as car drivers and airplane pilots, I clarified the effects of social robots acting social behaviors on task performance, mental workload, and emotions. In addition, I focused on vitality forms, one of the parameters of social behaviors, and clarified the effects of different vitality forms of social robots' behavior on cognitive tasks.In cognitive tasks such as car drivers and airplane pilots, we clarified the effects of social robots acting in social behaviors on task performance, mental workload, and emotions, and showed that the presence of social robots can be effective in cognitive tasks. Furthermore, focusing on vitality forms, one of the parameters of social behaviors, we clarified the effects of different vitality forms of social robots' behaviors on cognitive tasks, and found that social robots with calm behaviors positively affected participants' facial expressions and improved their performance in a short-term memory task. Based on the results, I decided to adopt the configuration of a robot head, eliminating the torso from the social humanoid robot, iCub, considering the possibility of placement in a limited space such as cockpits of car or airplane. In designing the robot head, I developed a novel soft-material eyebrow that can be mounted on the iCub robot head to achieve continuous position and velocity changes, which is an important factor to express vitality forms. The novel eyebrows can express different vitality forms by changing the shape and velocity of the eyebrows, which was conventionally represented by the iCub's torso and arms. [Significance] The results of my research are important achievements that opens up the possibility of applying social robots to non-robotic industries such as automotive and aircraft. In addition, the newly developed soft-material eyebrows' precise shape and velocity changes have opened up new research possibilities in social robotics and social communication research themselves, enabling experiments with complex facial expressions that move beyond Ekman's simple facial expression changes definition, such as, joy, anger, sadness, and pleasure. Thus, the results of this research are one important step in both scientific and industrial applications. [Key-words] social robot, cognitive task, vitality form, robot head, facial expression, eyebro

Integrated Framework Design for Intelligent Human Machine Interaction

Human-computer interaction, sometimes referred to as Man-Machine Interaction, is a concept that emerged simultaneously with computers, or more generally machines. The methods by which humans have been interacting with computers have traveled a long way. New designs and technologies appear every day. However, computer systems and complex machines are often only technically successful, and most of the time users may find them confusing to use; thus, such systems are never used efficiently. Therefore, building sophisticated machines and robots is not the only thing someone has to address; in fact, more effort should be put to make these machines simpler for all kind of users, and generic enough to accommodate different types of environments. Thus, designing intelligent human computer interaction modules come to emerge. In this work, we aim to implement a generic framework (referred to as CIMF framework) that allows the user to control the synchronized and coordinated cooperative type of work that a set of robots can perform. Three robots are involved so far: Two manipulators and one mobile robot. The framework should be generic enough to be hardware independent and to allow the easy integration of new entities and modules. We also aim to implement the different building blocks for the intelligent manufacturing cell that communicates with the framework via the most intelligent and advanced human computer interaction techniques. Three techniques shall be addressed: Interface-, audio-, and visual-based type of interaction

Observations on the dynamic control of an articulatory synthesizer using speech production data

This dissertation explores the automatic generation of gestural score based control structures for a three-dimensional articulatory speech synthesizer. The gestural scores are optimized in an articulatory resynthesis paradigm using a dynamic programming algorithm and a cost function which measures the deviation from a gold standard in the form of natural speech production data. This data had been recorded using electromagnetic articulography, from the same speaker to which the synthesizer\u27s vocal tract model had previously been adapted. Future work to create an English voice for the synthesizer and integrate it into a text-to-speech platform is outlined.Die vorliegende Dissertation untersucht die automatische Erzeugung von gesturalpartiturbasierten Steuerdaten für ein dreidimensionales artikulatorisches Sprachsynthesesystem. Die gesturalen Partituren werden in einem artikulatorischen Resynthese-Paradigma mittels dynamischer Programmierung optimiert, unter Zuhilfenahme einer Kostenfunktion, die den Abstand zu einem "Gold Standard" in Form natürlicher Sprachproduktionsdaten mißt. Diese Daten waren mit elektromagnetischer Artikulographie am selben Sprecher aufgenommen worden, an den zuvor das Vokaltraktmodell des Synthesesystems angepaßt worden war. Weiterführende Forschung, eine englische Stimme für das Synthesesystem zu erzeugen und sie in eine Text-to-Speech-Plattform einzubetten, wird umrissen

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14]

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Puhesynteesin ja suun liikkeen toteutus robotille

Tiivistelmä. Ihmiset kommunikoivat keskenään sekä verbaalisesti, että nonverbaalisesti. Ihmisten verbaalisen viestinnän, eli puheen, ja ihmisäänen tutkimusta kutsutaan fonetiikaksi ja sitä hyödynnetään robotiikassa keinotekoisen puheen eli puhesynteesin toteuttamisessa. Sosiaalisten robottien yleistyessä on tärkeää, että ihminen-robotti-vuorovaikutus olisi mahdollisimman luontevaa. Varsinkin kasvot ovat avainasemassa vuorovaikutuksessa, ja robotin visuaalinen suu tekee vuorovaikutuksesta ihmiselle mielekkäämpää, sekä auttaa puheen ymmärtämisessä. Myös robotin puheen ja leuan liikkeen synkronointi on tärkeässä roolissa ’uncanny valley’ -ilmiön välttämisessä. Tässä projektissa toteutettiin puhesynteesi ja leuan liike InMoov-robotille. Leuan liike toteutettiin Dynamixel XL-320-servolla, jota kontrolloitiin Arduino UNO:lla. Puhesynteeseinä toteutettiin formanttisynteesi ja konkatenaatiosynteesi sekä mies-, että naisäänellä. Molempia puhesynteesejä sekä puheen ja suun liikkeen synkronisaatiota arvioitiin subjektiivisesti itse kehitetyillä numeerisilla asteikoilla. Konkatenaatiosynteesi saavutti arvosanan 3/5, mikä tarkoittaa, että puheen välittämä viesti on ymmärrettävissä, formanttisynteesi arvosanan 2/5, mikä tarkoittaa, että puheen välittämä viesti jää epäselväksi, ja synkronisaatio arvosanan 3/4, mikä tarkoittaa, että synkronisaatio on suurimman osan ajasta hyvä. Projektissa hyödynnettiin apuna metakäyttöjärjestelmää Robot Operating System (ROS). Ohjelmistokomponenteista toteutettiin ROS-paketti, jonka toiminnallisuus koostuu kahdesta keskenään kommunikoivasta ROS-solmusta, joista toinen vastaa servon kontrolloimisesta ja toinen puhesynteesistä. Projektissa toteutettuja komponentteja voidaan hyödyntää tulevaisuudessa esimerkiksi ihminen-robotti-vuorovaikutuksen tutkimiseen ja erilaisten havaintoesitysten toteuttamiseen. Jatkossa puhesynteesin ja leuan liikkeen synkronisaatiota voitaisiin parantaa toteuttamalla monipuolisempia leuan asentoja sekä selkeyttämällä puhesynteesejä.Abstract. Humans communicate with each other both verbally and non-verbally. The research of verbal human communication and human voice is called phonetics and it can be utilized in robotics to produce artificial speech, ergo speech synthesis. As social robots become more common it is important for human-robot interaction to be as natural as possible. Especially faces are key components in interaction and robot’s visual mouth makes the interaction more pleasant to humans and helps them in understanding speech better. The synchronisation of the robot’s speech and mouth movement also plays an important role in avoiding the ’uncanny valley’ phenomenon. In this project two speech syntheses and motion of jaw were implemented for InMoov-robot. The motion of the jaw was implemented with Dynamixel’s XL-320 servomotor controlled by Arduino UNO. Two different speech syntheses were implemented: formant synthesis and concatenation synthesis with both female and male voices. Both of the speech syntheses and the synchronization of the speech and mouth movement were evaluated on self-made subjective numerical scales. The concatenation synthesis accomplished a grade of 3/5, which means that the speech was comprehensible. The formant synthesis accomplished a grade of 2/5, which means the speech was quite incoherent. Last but not least, the synchronization accomplished a grade of 3/4, which means the synchronization was good most of the time. The project utilizes a meta-operating system called Robot Operating System (ROS). The two software components implemented in this project comprise a ROS package consisting of two ROS nodes; one responsible for servo control and the other for speech synthesis. In the future these components can be utilized in research of human-robot interaction and in different demonstrations. The synchronisation of the jaw movement and speech could be improved by implementing more options for the position of the jaw and by enhancing the speech syntheses to sound more natural