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To deal with the question of what a sociable robot is, we describe how an educational robot is encountered by children, teachers and designers in a preschool. We consider the importance of the robot’s body by focusing on how its movements are contingently embedded in interactional situations. We point out that the effects of agency that these movements generate are inseparable from their grounding in locally coordinated, multimodal actions and interactions. |
Sociable robots are benefiting from machine perception systems that automatically recognize social behavior (e.g., detect and recognize people, recognize their facial expressions and gestures).
1 aMovellan, J. 1 aMalmir, M. 1 aForster, D. uhttps://rubi.ucsd.edu/content/hri-tool-monitor-socio-emotional-development-early-childhood-education 00519nas a2200145 4500008004100000245007400041210006900115260000900184100001500193700001600208700001900224700001700243700001700260856009600277 2013 eng d 00 aHome Alone: Social Robots for Digital Ethnography of Toddler Behavior 0 aHome Alone Social Robots for Digital Ethnography of Toddler Beha bIEEE 1 aMalmir, M. 1 aForster, D. 1 aYoungstrom, K. 1 aMorrison, L. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/home-alone-social-robots-digital-ethnography-toddler-behavior 00540nas a2200157 4500008004100000020002200041245006200063210006100125260003300186100001600219700001500235700001600250700001400266700001700280856008500297 2012 eng d a978-1-4673-4964-2 00 aDesign and early evaluation of the RUBI-5 sociable robots 0 aDesign and early evaluation of the RUBI5 sociable robots aSan Diego, CA bIEEE c11/2012 1 aJohnson, D. 1 aMalmir, M. 1 aForster, D. 1 aAlač, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/design-and-early-evaluation-rubi-5-sociable-robots 01907nas a2200373 4500008004100000020002200041245007400063210006900137260003700206520070300243653003700946653003000983653003501013653003001048653001201078653002001090653001401110653002401124653002101148653001001169653003101179653001301210653002101223653000901244653001701253653003601270653003501306653002601341100001401367700002001381700001801401700001701419856009701436 2011 eng d a978-1-4244-9140-7 00 aAutomated facial affect analysis for one-on-one tutoring applications 0 aAutomated facial affect analysis for oneonone tutoring applicati aSanta Barbara, CA bIEEE c03/2011 3 aIn this paper, we explore the use of computer vision techniques to analyze students' moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student's mood and affective state. We find that the problem of accurately determining a child's mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.
10 aautomated facial affect analysis 10 aautomated tutoring system 10 abehavioural sciences computing 10 acomputer vision technique 10 aContext 10 adecision making 10 aeducation 10 aEmotion recognition 10 aface recognition 10 aHuman 10 ahuman computer interaction 10 aLabeling 10 aMachine Learning 10 aMood 10 an Histograms 10 aone-on-one tutoring application 10 as Intelligent tutoring systems 10 astudent mood analysis 1 aButko, N. 1 aTheocharous, G. 1 aPhilipose, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/automated-facial-affect-analysis-one-one-tutoring-applications 02299nas a2200445 4500008004100000020002200041245005500063210004900118260003700167520099600204653001901200653001301219653005401232653000901286653004401295653001401339653002101353653002401374653001301398653002501411653000901436653002101445653003701466653003001503653002001533653000901553653000901562653001701571653001801588653001901606653003901625100001901664700001801683700001101701700001401712700001401726700001701740700001701757856007901774 2011 eng d a978-1-4244-9140-7 00 aThe computer expression recognition toolbox (CERT) 0 acomputer expression recognition toolbox CERT aSanta Barbara, CA bIEEE c03/2011 3 aWe present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different prototypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+[1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80%. In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second.
10 a3D orientation 10 aAccuracy 10 aautomatic real-time facial expression recognition 10 aCERT 10 acomputer expression recognition toolbox 10 aDetectors 10 adual core laptop 10 aEmotion recognition 10 aEncoding 10 aextended Cohn-Kanade 10 aFace 10 aface recognition 10 afacial action unit coding system 10 afacial expression dataset 10 aFacial features 10 aFACS 10 aGold 10 aImage coding 10 asoftware tool 10 asoftware tools 10 atwo-alternative forced choice task 1 aLittlewort, G. 1 aWhitehill, J. 1 aWu, T. 1 aFasel, I. 1 aFrank, M. 1 aMovellan, J. 1 aBartlett, M. uhttps://rubi.ucsd.edu/content/computer-expression-recognition-toolbox-cert 01906nas a2200265 4500008004100000022002500041245011900066210006900185260001200254300001200266490000700278520104500285653000901330653001101339653001201350653003001362653001501392653001801407653002001425653002501445100001401470700001701484700001501501856012401516 2011 eng d a0306-3127, 1460-3659 00 aWhen a robot is social: Spatial arrangements and multimodal semiotic engagement in the practice of social robotics 0 aWhen a robot is social Spatial arrangements and multimodal semio c12/2011 a893-926 0 v41 3 aSocial roboticists design their robots to function as social agents in interaction with humans and other robots. Although we do not deny that the robot’s design features are crucial for attaining this aim, we point to the relevance of spatial organization and coordination between the robot and the humans who interact with it. We recover these interactions through an observational study of a social robotics laboratory and examine them by applying a multimodal interactional analysis to two moments of robotics practice. We describe the vital role of roboticists and of the group of preverbal infants, who are involved in a robot’s design activity, and we argue that the robot’s social character is intrinsically related to the subtleties of human interactional moves in laboratories of social robotics. This human involvement in the robot’s social agency is not simply controlled by individual will. Instead, the human–machine couplings are demanded by the situational dynamics in which the robot is lodged.
10 abody 10 adesign 10 agesture 10 ahuman–robot interaction 10 alaboratory 10 asocial agency 10 asocial robotics 10 aspatial organization 1 aAlač, M. 1 aMovellan, J. 1 aTanaka, F. uhttps://rubi.ucsd.edu/content/when-robot-social-spatial-arrangements-and-multimodal-semiotic-engagement-practice-social 02633nas a2200397 4500008004100000022001400041245003700055210003700092300001100129490000600140520152800146653003301674653003201707653003001739653002001769653002601789653001701815653002601832653001901858653001001877653002001887653001701907653002101924653002001945653003601965653002002001653002402021653001902045653001802064653002202082653001802104653001802122100001402140700001702154856006402171 2010 eng d a1943-0604 00 aInfomax Control of Eye Movements 0 aInfomax Control of Eye Movements a91-107 0 v2 3 aRecently, infomax methods of optimal control have begun to reshape how we think about active information gathering. We show how such methods can be used to formulate the problem of choosing where to look. We show how an optimal eye movement controller can be learned from subjective experiences of information gathering, and we explore in simulation properties of the optimal controller. This controller outperforms other eye movement strategies proposed in the literature. The learned eye movement strategies are tailored to the specific visual system of the learner-we show that agents with different kinds of eyes should follow different eye movement strategies. Then we use these insights to build an autonomous computer program that follows this approach and learns to search for faces in images faster than current state-of-the-art techniques. The context of these results is search in static scenes, but the approach extends easily, and gives further efficiency gains, to dynamic tracking tasks. A limitation of infomax methods is that they require probabilistic models of uncertainty of the sensory system, the motor system, and the external world. In the final section of this paper, we propose future avenues of research by which autonomous physical agents may use developmental experience to subjectively characterize the uncertainties they face.
10 aactive information gathering 10 aautonomous computer program 10 aautonomous physical agent 10 aComputer vision 10 adynamic tracking task 10 aEye movement 10 aeye movement strategy 10 aface detection 10 afaces 10 aInfomax control 10 amotor system 10 aobject detection 10 aoptimal control 10 aoptimal eye movement controller 10 apolicy gradient 10 aprobabilistic model 10 asensory system 10 astatic scenes 10 aVisual Perception 10 avisual search 10 avisual system 1 aButko, N. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/infomax-control-eye-movements 00449nas a2200109 4500008004100000245009000041210006900131300001200200490000700212100001700219856010300236 2010 eng d 00 aWarning: The author of this document may have no mental states. Read at your own risk 0 aWarning The author of this document may have no mental states Re a238-245 0 v11 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/warning-author-document-may-have-no-mental-states-read-your-own-risk 01476nas a2200157 4500008004100000245004600041210004600087300001200133490000800145520103300153100001701186700001301203700001701216700001801233856006701251 2009 eng d 00 aFoundations for a New Science of Learning 0 aFoundations for a New Science of Learning a284-288 0 v325 3 aHuman learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved compared with those of other species. Homo sapiens is also the only species that has developed formal ways to enhance learning: teachers, schools, and curricula. Human infants have an intense interest in people and their behavior and possess powerful implicit learning mechanisms that are affected by social interaction. Neuroscientists are beginning to understand the brain mechanisms underlying learning and how shared brain systems for perception and action support social learning. Machine learning algorithms are being developed that allow robots and computers to learn autonomously. New insights from many different fields are converging to create a new science of learning that may transform educational practices.
1 aMeltzoff, A. 1 aKuhl, P. 1 aMovellan, J. 1 aSejnowski, T. uhttps://rubi.ucsd.edu/content/foundations-new-science-learning 01688nas a2200385 4500008004100000020002200041245004000063210004000103260002800143520060700171653001400778653002400792653001900816653002100835653002800856653001300884653001100897653003900908653002100947653001900968653001500987653004401002653002601046653001701072653001101089653002501100653002001125653001601145100001101161700001401172700001501186700001701201700001701218856006701235 2009 eng d a978-1-4244-4117-4 00 aLearning to Make Facial Expressions 0 aLearning to Make Facial Expressions aShanghai bIEEE c06/2009 3 aThis paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate the computational study of how infants learn to make facial expressions.
10 aActuators 10 aEmotion recognition 10 aface detection 10 aface recognition 10 afacial motor parameters 10 aFeedback 10 aHumans 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMagnetic heads 10 aPediatrics 10 areal-time facial expression recognition 10 aRobot sensing systems 10 arobotic head 10 aRobots 10 aself-guided learning 10 aServomechanisms 10 aServomotors 1 aWu, T. 1 aButko, N. 1 aRuvulo, P. 1 aBartlett, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/learning-make-facial-expressions 01916nas a2200373 4500008004100000020002200041245005400063210005400117260003200171520077900203653001500982653003200997653003701029653002901066653002301095653001001118653001801128653002801146653002401174653001501198653002601213653001301239653001901252653003301271653001601304653003001320653002701350653001501377100001701392700001701409700001501426700001701441856008401458 2009 eng d a978-1-60558-404-1 00 aSociable robot improves toddler vocabulary skills 0 aSociable robot improves toddler vocabulary skills aLa Jolla, CA bIEEE c03/2009 3 aWe report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27% improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments.
10 aAlgorithms 10 aautonomously operated robot 10 aEarly Childhood Education Center 10 aEducational institutions 10 aEducational robots 10 aGames 10 ahuman factors 10 aHuman-robot interaction 10 aintervention period 10 aPediatrics 10 aRobot sensing systems 10 arobotics 10 asociable robot 10 asocial aspects of automation 10 atime 2 week 10 atoddler vocabulary skills 10 aUbiquitous computering 10 aVocabulary 1 aMovellan, J. 1 aEckhardt, M. 1 aVirnes, M. 1 aRodriguez, A uhttps://rubi.ucsd.edu/content/sociable-robot-improves-toddler-vocabulary-skills 03033nas a2200565 4500008004100000022001400041245003700055210003700092260001200129300001400141490000700155520134500162653001501507653002801522653001401550653005301564653003501617653001301652653002401665653002001689653002201709653000901731653003301740653002101773653002701794653004801821653002801869653002001897653002201917653002501939653004401964653003902008653003202047653002802079653001302107653002102120653003002141653003102171653003202202653001202234653002602246653002202272653002102294100001802315700001902333700001402352700001702366700001702383856006702400 2009 eng d a0162-8828 00 aToward Practical Smile Detection 0 aToward Practical Smile Detection c11/2009 a2106-2111 0 v31 3 aMachine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.
10 aAlgorithms 10 aArtificial intelligence 10 aAutomated 10 aautomatic facial expression recognition research 10 aBiological Pattern Recognition 10 aBiometry 10 aComputer simulation 10 aComputer vision 10 aComputer-Assisted 10 aFace 10 aFace and gesture recognition 10 aface recognition 10 afeature representation 10 ahuman-level expression recognition accuracy 10 aillumination conditions 10 aImage databases 10 aImage Enhancement 10 aImage Interpretation 10 aimage registration image representation 10 alearning (artificial intelligence) 10 amachine learning approaches 10 aMachine Learning Models 10 an Humans 10 aobject detection 10 apractical smile detection 10 aReproducibility of Results 10 aSensitivity and Specificity 10 aSmiling 10 aSubtraction Technique 10 atraining data set 10 avisual databases 1 aWhitehill, J. 1 aLittlewort, G. 1 aFasel, I. 1 aBartlett, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/toward-practical-smile-detection 02714nas a2200361 4500008004100000020002200041245006200063210006200125260003200187520158600219653002801805653002001833653002201853653002301875653002401898653003001922653001901952653001201971653003601983653003902019653002102058653002002079653002302099653003502122653001602157653001702173653001102190653001702201100001502218700001402233700001702247856008802264 2008 eng d a978-1-4244-1646-2 00 aAuditory mood detection for social and educational robots 0 aAuditory mood detection for social and educational robots aPasadena, CA bIEEE c05/2008 3 aSocial robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al., 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al., 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.
10 aauditory mood detection 10 aComputer vision 10 aeducational robot 10 aEducational robots 10 aEmotion recognition 10 aemotional speech database 10 aface detection 10 ahearing 10 ainteractive robotic application 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMood Prototypes 10 aobject recognition 10 aRobotics and Automation Robots 10 asocial mood 10 asocial robot 10 aSpeech 10 aUSA Councils 1 aRuvolo, P. 1 aFasel, I. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/auditory-mood-detection-social-and-educational-robots 01620nas a2200349 4500008004100000020002200041245006600063210006600129260003200195520052200227653001900749653001900768653002800787653003500815653001300850653003900863653001400902653002300916653002400939653001700963653001100980653003900991653002101030653000901051653002501060653001501085653001501100653003001115100001501145700001701160856009301177 2008 eng d a978-1-4244-2661-4 00 aAutomatic cry detection in early childhood education settings 0 aAutomatic cry detection in early childhood education settings aMonterey, CA bIEEE c08/2008 3 aWe present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
10 aAcoustic noise 10 aauditory moods 10 aautomatic cry detection 10 abehavioural sciences computing 10 aDeafness 10 aearly childhood education settings 10 aeducation 10 aEducational robots 10 aEmotion recognition 10 ahuman coders 10 aHumans 10 alearning (artificial intelligence) 10 aMachine Learning 10 aMood 10 apreschool classrooms 10 aPrototypes 10 aRobustness 10 aWorking environment noise 1 aRuvolo, P. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/automatic-cry-detection-early-childhood-education-settings 01706nas a2200337 4500008004100000020002200041245008400063210006900147260002600216520061900242653001400861653003400875653003300909653001000942653001400952653002800966653003600994653002001030653001301050653002001063653002401083653001501107653002301122653001801145653002601163653001501189653002301204100001501227700001701242856010901259 2008 eng d a978-1-4244-2212-8 00 aA barebones communicative robot based on social contingency and Infomax Control 0 abarebones communicative robot based on social contingency and In aMunich bIEEE c08/2008 3 aIn this paper, we present a barebones robot which is capable of interacting with humans based on social contingency. It expands the previous work of a contingency detector into having both human-model updating (developmental capability) and policy improvement (learning capability) based on the framework of Infomax control. The proposed new controller interacts with humans in both active and responsive ways handling the turn-taking between them.
10 aActuators 10 abarebones communicative robot 10 aCommunication system control 10 aDelay 10 aDetectors 10 aHuman robot interaction 10 ahuman-model updating capability 10 ahumanoid robots 10 aHydrogen 10 aInfomax control 10 aman-machine systems 10 aPediatrics 10 apolicy improvement 10 aRobot control 10 aRobot sensing systems 10 aScheduling 10 asocial contingency 1 aTanaka, F. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/barebones-communicative-robot-based-social-contingency-and-infomax-control 02582nas a2200373 4500008004100000020002200041245007500063210006900138260003100207520141200238653002801650653003201678653002201710653001601732653001001748653001401758653002301772653002001795653001601815653002801831653002001859653002801879653002301907653001901930653002801949653002201977653003001999653001102029100001502040700001802055700001502073700001702088856010302105 2008 eng d a978-1-4244-2661-4 00 aBuilding a more effective teaching robot using apprenticeship learning 0 aBuilding a more effective teaching robot using apprenticeship le aMonterey, CA bIEE c08/2008 3 aWhat defines good teaching? While attributes such as timing, responsiveness to social cues, and pacing of material clearly play a role, it is difficult to create a comprehensive specification of what it means to be a good teacher. On the other hand, it is relatively easy to obtain examples of expert teaching behavior by observing a real teacher. With this inspiration as our guide, we investigated apprenticeship learning methods [1] that use data recorded from expert teachers as a means of improving the teaching abilities of RUBI, a social robot immersed in a classroom of 18-24 month old children. While this approach has achieved considerable success in mechanical control, such as automated helicopter flight [2], until now there has been little work on applying it to the field of social robotics. This paper explores two particular approaches to apprenticeship learning, and analyzes the models of teaching that each approach learns from the data of the human teacher. Empirical results indicate that the apprenticeship learning paradigm, though still nascent in its use in the social robotics field, holds promise, and that our proposed methods can already extract meaningful teaching models from demonstrations of a human expert.
10 aapprenticeship learning 10 aautomated helicopter flight 10 aAutomatic control 10 aData mining 10 aDelay 10 aeducation 10 aEducational robots 10 aexpert teaching 10 aHelicopters 10 aHuman-robot interaction 10 ahumanoid robots 10 aHumans Learning systems 10 amechanical control 10 arobot teaching 10 aRobotics and Automation 10 aRUBI social robot 10 atime 18 month to 24 month 10 atiming 1 aRuvolo, P. 1 aWhitehill, J. 1 aVirnes, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/building-more-effective-teaching-robot-using-apprenticeship-learning 02735nas a2200409 4500008004100000020002200041245004400063210004400107260003200151520155400183653002501737653002501762653001801787653002101805653001901826653001901845653001201864653002801876653002901904653002701933653001501960653002301975653002701998653001102025653002202036653001802058653001702076653002502093653002402118653002502142653002602167100001402193700001402207700001702221700001702238856007002255 2008 eng d a978-1-4244-1646-2 00 aVisual saliency model for robot cameras 0 aVisual saliency model for robot cameras aPasadena, CA bIEEE c05/2008 3 aRecent years have seen an explosion of research on the computational modeling of human visual attention in task free conditions, i.e., given an image predict where humans are likely to look. This area of research could potentially provide general purpose mechanisms for robots to orient their cameras. One difficulty is that most current models of visual saliency are computationally very expensive and not suited to real time implementations needed for robotic applications. Here we propose a fast approximation to a Bayesian model of visual saliency recently proposed in the literature. The approximation can run in real time on current computers at very little computational cost, leaving plenty of CPU cycles for other tasks. We empirically evaluate the saliency model in the domain of controlling saccades of a camera in social robotics situations. The goal was to orient a camera as quickly as possible toward human faces. We found that this simple general purpose saliency model doubled the success rate of the camera: it captured images of people 70% of the time, when compared to a 35% success rate when the camera was controlled using an open-loop scheme. After 3 saccades (camera movements), the robot was 96% likely to capture at least one person. The results suggest that visual saliency models may provide a useful front end for camera control in robotics applications.
10 aApplication software 10 aapproximation theory 10 aBayes methods 10 aBayesian methods 10 aBayesian model 10 acamera control 10 aCameras 10 aCentral Processing Unit 10 aComputational efficiency 10 aComputational modeling 10 aExplosions 10 afast approximation 10 ahuman visual attention 10 aHumans 10 aOpen loop systems 10 arobot cameras 10 arobot vision 10 aRobot vision systems 10 arobotic application 10 atask free conditions 10 avisual saliency model 1 aButko, N. 1 aZhang, L. 1 aCottrell, G. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/visual-saliency-model-robot-cameras 00263nas a2200097 4500008004100000245002200041210002200063100001400085700001700099856004900116 2007 eng d 00 aLearning to Learn 0 aLearning to Learn 1 aButko, N. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/learning-learn 00392nas a2200133 4500008004100000245004000041210003500081100001500116700001700131700001500148700001500163700001700178856006300195 2007 eng d 00 aThe RUBI Project: A Progress Report 0 aRUBI Project A Progress Report 1 aTanaka, F. 1 aMovellan, J. 1 aTaylor, C. 1 aRuvolo, P. 1 aEckhardt, M. uhttps://rubi.ucsd.edu/content/rubi-project-progress-report 00483nas a2200121 4500008004100000245008500041210006900126490000800195100001500203700001700218700001700235856010900252 2007 eng d 00 aSocialization between toddlers and robots at an early childhood education center 0 aSocialization between toddlers and robots at an early childhood 0 v104 1 aTanaka, F. 1 aCicourel, A. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/socialization-between-toddlers-and-robots-early-childhood-education-center 00548nas a2200109 4500008004100000245013200041210006900173260003800242100001500280700001700295856012600312 2006 eng d 00 aBehavior Analysis of Children’s Touch on a Small Humanoid Robot: Long-term Observation at a Daily Classroom over Three Months 0 aBehavior Analysis of Children s Touch on a Small Humanoid Robot aUnited Kingdom bHatfield c09/2006 1 aTanaka, F. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/behavior-analysis-children%E2%80%99s-touch-small-humanoid-robot-long-term-observation-daily 00541nas a2200133 4500008004100000245009600041210006900137260001900206100001500225700001700240700002000257700001500277856011500292 2006 eng d 00 aDaily HRI evaluation at a classroom environment: Reports from dance interaction experiments 0 aDaily HRI evaluation at a classroom environment Reports from dan aSalt Lake City 1 aTanaka, F. 1 aMovellan, J. 1 aFortenberry, B. 1 aAisaka, K. uhttps://rubi.ucsd.edu/content/daily-hri-evaluation-classroom-environment-reports-dance-interaction-experiments 00415nas a2200109 4500008004100000245006100041210005600102260003200158100001500190700001700205856008300222 2006 eng d 00 aThe RUBI Project: Designing Everyday Robots by Immersion 0 aRUBI Project Designing Everyday Robots by Immersion aBloomington, U.S.A c06/2006 1 aTanaka, F. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/rubi-project-designing-everyday-robots-immersion 00635nas a2200157 4500008004100000245011300041210006900154260003100223300001200254653002100266100001500287700002000302700001500322700001700337856012300354 2005 eng d 00 aDeveloping Dance Interaction between QRIO and Toddlers in a Classroom Environment: Plans for the First Steps 0 aDeveloping Dance Interaction between QRIO and Toddlers in a Clas aNashville, U.S.A. c08/2005 a223-228 10 aBest Paper Award 1 aTanaka, F. 1 aFortenberry, B. 1 aAisaka, K. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/developing-dance-interaction-between-qrio-and-toddlers-classroom-environment-plans-first 00346nas a2200109 4500008004100000245004000041210003900081100001400120700002000134700001700154856006500171 2005 eng d 00 aMPT: the Machine Perception Toolbox 0 aMPT the Machine Perception Toolbox 1 aFasel, I. 1 aFortenberry, B. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/mpt-machine-perception-toolbox 00561nas a2200133 4500008004100000245010600041210006900147260001700216100001500233700002000248700001500268700001700283856012700300 2005 eng d 00 aPlans for developing real-time dance interaction between qrio and toddlers in a classroom environment 0 aPlans for developing realtime dance interaction between qrio and aOsaka, Japan 1 aTanaka, F. 1 aFortenberry, B. 1 aAisaka, K. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/plans-developing-real-time-dance-interaction-between-qrio-and-toddlers-classroom-environment 00451nas a2200133 4500008004100000245005800041210005200099260001700151100001700168700001500185700002000200700001500220856008200235 2005 eng d 00 aThe RUBI project: Origins, principles and first steps 0 aRUBI project Origins principles and first steps aOsaka, Japan 1 aMovellan, J. 1 aTanaka, F. 1 aFortenberry, B. 1 aAisaka, K. uhttps://rubi.ucsd.edu/content/rubi-project-origins-principles-and-first-steps 00540nas a2200133 4500008004100000245010000041210006900141100001900210700001700229700001400246700001400260700001700274856011500291 2004 eng d 00 aAnalysis of machine learning methods for real-time recognition of facial expressions from video 0 aAnalysis of machine learning methods for realtime recognition of 1 aLittlewort, G. 1 aBartlett, M. 1 aFasel, I. 1 aChenu, J. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/analysis-machine-learning-methods-real-time-recognition-facial-expressions-video 00668nas a2200181 4500008004100000245012300041210006900164300001400233490000700247100001900254700001700273700001400290700001400304700001400318700001700332700001700349856012000366 2004 eng d 00 aTowards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification 0 aTowards social robots Automatic evaluation of humanrobot interac a1563-1570 0 v16 1 aLittlewort, G. 1 aBartlett, M. 1 aChenu, J. 1 aFasel, I. 1 aKanda, T. 1 aIshiguro, H. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/towards-social-robots-automatic-evaluation-human-robot-interaction-face-detection-and