Intelligent Management of Hierarchical Behaviors Using a NAO Robot as a Vocational Tutor

In order to create an intelligent system which can hold an interview using the NAO robot as an interviewer playing the role of a vocational tutor were classified and categorized twenty behaviors within five personality profiles. Five basic emotions are considered: Anger, boredom, interest, surprise and joy. Selected behaviors are grouped according to these five different emotions. Common behaviors (e.g., movements or body postures) used by the robot (who assumes the role of vocational tutor) during vocational guidance sessions will be based on a theory of personality traits called the "Five Factor Model". In this context, a predefined set of questions will be asked by the robot according to a theoretical model called "Orientation Model" about the person's vocational preferences. Therefore, NAO can react as conveniently as possible during the interview according to the score of the answer given by the person to the question posed and its personality type. Additionally, based on the answers to these questions, it is established a vocational profile and the robot can to emit a recommendation about person vocation. The results obtained show how the intelligent selection of behaviors can be successfully achieved through the proposed approach, making the interaction between a human and a robot friendlier

Investigating the Effects of Robot Engagement Communication on Learning from Demonstration

Robot Learning from Demonstration (RLfD) is a technique for robots to derive policies from instructors' examples. Although the reciprocal effects of student engagement on teacher behavior are widely recognized in the educational community, it is unclear whether the same phenomenon holds true for RLfD. To fill this gap, we first design three types of robot engagement behavior (attention, imitation, and a hybrid of the two) based on the learning literature. We then conduct, in a simulation environment, a within-subject user study to investigate the impact of different robot engagement cues on humans compared to a "without-engagement" condition. Results suggest that engagement communication significantly changes the human's estimation of the robots' capability and significantly raises their expectation towards the learning outcomes, even though we do not run actual learning algorithms in the experiments. Moreover, imitation behavior affects humans more than attention does in all metrics, while their combination has the most profound influences on humans. We also find that communicating engagement via imitation or the combined behavior significantly improve humans' perception towards the quality of demonstrations, even if all demonstrations are of the same quality.Comment: Under revie

Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe

Human-centred design methods : developing scenarios for robot assisted play informed by user panels and field trials

Original article can be found at: http://www.sciencedirect.com/ Copyright ElsevierThis article describes the user-centred development of play scenarios for robot assisted play, as part of the multidisciplinary IROMEC1 project that develops a novel robotic toy for children with special needs. The project investigates how robotic toys can become social mediators, encouraging children with special needs to discover a range of play styles, from solitary to collaborative play (with peers, carers/teachers, parents, etc.). This article explains the developmental process of constructing relevant play scenarios for children with different special needs. Results are presented from consultation with panel of experts (therapists, teachers, parents) who advised on the play needs for the various target user groups and who helped investigate how robotic toys could be used as a play tool to assist in the children’s development. Examples from experimental investigations are provided which have informed the development of scenarios throughout the design process. We conclude by pointing out the potential benefit of this work to a variety of research projects and applications involving human–robot interactions.Peer reviewe

Persuasiveness of social robot ‘Nao’ based on gaze and proximity

Social Robots have widely infiltrated the retail and public space. Mainly, social robots are being utilized across a wide range of scenarios to influence decision making, disseminate information, and act as a signage mechanism, under the umbrella of Persuasive Robots or Persuasive Technology. While there have been several studies in the afore-mentioned area, the effect of non-verbal behaviour on persuasive abilities is generally unexplored. Therefore, in this research, we report whether two key non-verbal attributes, namely proximity and gaze, can elicit persuasively, compliance, and specific personality appeals. For this, we conducted a 2 (eye gaze) x 2 (proximity) between-subjects experiment where participants viewed a video-based scenario of the Nao robot. Our initial results did not reveal any significant results based on the non-verbal attributes. However, perceived compliance and persuasion were significantly correlated with knowledge, responsiveness, and trustworthiness. In conclusion, we discuss how the design of a robot could make it more convincing as extensive marketing and brand promotion companies could use robots to enhance their advertisement operations

Metrics to Evaluate Human Teaching Engagement From a Robot's Point of View

This thesis was motivated by a study of how robots can be taught by humans, with an emphasis on allowing persons without programming skills to teach robots. The focus of this thesis was to investigate what criteria could or should be used by a robot to evaluate whether a human teacher is (or potentially could be) a good teacher in robot learning by demonstration. In effect, choosing the teacher that can maximize the benefit to the robot using learning by imitation/demonstration. The study approached this topic by taking a technology snapshot in time to see if a representative example of research laboratory robot technology is capable of assessing teaching quality. With this snapshot, this study evaluated how humans observe teaching quality to attempt to establish measurement metrics that can be transferred as rules or algorithms that are beneficial from a robot’s point of view. To evaluate teaching quality, the study looked at the teacher-student relationship from a human-human interaction perspective. Two factors were considered important in defining a good teacher: engagement and immediacy. The study gathered more literature reviews relating to further detailed elements of engagement and immediacy. The study also tried to link physical effort as a possible metric that could be used to measure the level of engagement of the teachers. An investigatory experiment was conducted to evaluate which modality the participants prefer to employ in teaching a robot if the robot can be taught using voice, gesture demonstration, or physical manipulation. The findings from this experiment suggested that the participants appeared to have no preference in terms of human effort for completing the task. However, there was a significant difference in human enjoyment preferences of input modality and a marginal difference in the robot’s perceived ability to imitate. A main experiment was conducted to study the detailed elements that might be used by a robot in identifying a “good” teacher. The main experiment was conducted in two subexperiments. The first part recorded the teacher’s activities and the second part analysed how humans evaluate the perception of engagement when assessing another human teaching a robot. The results from the main experiment suggested that in human teaching of a robot (human-robot interaction), humans (the evaluators) also look for some immediacy cues that happen in human-human interaction for evaluating the engagement

Designing Sound for Social Robots: Advancing Professional Practice through Design Principles

Sound is one of the core modalities social robots can use to communicate with the humans around them in rich, engaging, and effective ways. While a robot's auditory communication happens predominantly through speech, a growing body of work demonstrates the various ways non-verbal robot sound can affect humans, and researchers have begun to formulate design recommendations that encourage using the medium to its full potential. However, formal strategies for successful robot sound design have so far not emerged, current frameworks and principles are largely untested and no effort has been made to survey creative robot sound design practice. In this dissertation, I combine creative practice, expert interviews, and human-robot interaction studies to advance our understanding of how designers can best ideate, create, and implement robot sound. In a first step, I map out a design space that combines established sound design frameworks with insights from interviews with robot sound design experts. I then systematically traverse this space across three robot sound design explorations, investigating (i) the effect of artificial movement sound on how robots are perceived, (ii) the benefits of applying compositional theory to robot sound design, and (iii) the role and potential of spatially distributed robot sound. Finally, I implement the designs from prior chapters into humanoid robot Diamandini, and deploy it as a case study. Based on a synthesis of the data collection and design practice conducted across the thesis, I argue that the creation of robot sound is best guided by four design perspectives: fiction (sound as a means to convey a narrative), composition (sound as its own separate listening experience), plasticity (sound as something that can vary and adapt over time), and space (spatial distribution of sound as a separate communication channel). The conclusion of the thesis presents these four perspectives and proposes eleven design principles across them which are supported by detailed examples. This work contributes an extensive body of design principles, process models, and techniques providing researchers and designers with new tools to enrich the way robots communicate with humans

로봇의 신체 언어가 사회적 특성과 인간 유사성에 미치는 영향

학위논문 (석사) -- 서울대학교 대학원 : 사회과학대학 심리학과, 2021. 2. Sowon Hahn.The present study investigated the role of robots’ body language on perceptions of social qualities and human-likeness in robots. In experiment 1, videos of a robot’s body language varying in expansiveness were used to evaluate the two aspects. In experiment 2, videos of social interactions containing the body languages in experiment 1 were used to further examine the effects of robots’ body language on these aspects. Results suggest that a robot conveying open body language are evaluated higher on perceptions of social characteristics and human-likeness compared to a robot with closed body language. These effects were not found in videos of social interactions (experiment 2), which suggests that other features play significant roles in evaluations of a robot. Nonetheless, current research provides evidence of the importance of robots’ body language in judgments of social characteristics and human-likeness. While measures of social qualities and human-likeness favor robots that convey open body language, post-experiment interviews revealed that participants expect robots to alleviate feelings of loneliness and empathize with them, which require more diverse body language in addition to open body language. Thus, robotic designers are encouraged to develop robots capable of expressing a wider range of motion. By enabling complex movements, more natural communications between humans and robots are possible, which allows humans to consider robots as social partners.본 연구는 로봇의 신체 언어가 사회적 특성과 인간과의 유사성에 대한 인간의 인식에 미치는 영향을 탐색하였다. 실험 1에서는 로봇의 개방적 신체 언어가 묘사된 영상과 폐쇄적 신체 언어가 묘사된 영상을 통해 이러한 세 가지 측면을 살펴보았다. 실험 2에서는 실험 1의 신체 언어가 포함된 로봇과 사람 간의 상호작용 영상을 활용하여 로봇의 신체 언어가 위 두 가지 측면에 미치는 영향을 탐색하였다. 결과적으로, 사람들은 폐쇄적 신체 언어를 표현하는 로봇에 비해 개방적 신체 언어를 표현하는 로봇을 사회적 특성과 인간과의 유사성에 대한 인식 면에서 더 높게 평가한다는 것을 확인하였다. 그러나 사람과의 상호작용을 담은 영상을 통해서는 이러한 효과가 발견되지 않았으며, 이는 실험 2에 포함된 음성 등의 다른 특징이 로봇에 대한 평가에 중요한 역할을 한다는 것을 시사한다. 그럼에도 불구하고, 본 연구는 로봇의 신체 언어가 사회적 특성 및 인간과의 유사성에 대한 인식의 중요한 요인이 된다는 근거를 제공한다. 사회적 특성과 인간과의 유사성의 척도에서는 개방적 신체 언어를 표현하는 로봇이 더 높게 평가되었지만, 실험 후 인터뷰에서는 로봇이 외로운 감정을 완화하고 공감하기를 기대하는 것으로 나타나 이 상황들에 적절한 폐쇄적 신체 언어 또한 배제할 수 없다고 해석할 수 있다. 이에 따라 본 연구에서는 로봇 디자이너들이 더욱 다양한 범위의 움직임을 표현할 수 있는 로봇을 개발하도록 장려한다. 그렇다면 섬세한 움직임에 따른 자연스러운 의사소통을 통해 인간이 로봇을 사회적 동반자로 인식할 수 있을 것이다.Chapter 1. Introduction 1 1. Motivation 1 2. Theoretical Background and Previous Research 3 3. Purpose of Study 12 Chapter 2. Experiment 1 13 1. Objective and Hypotheses 13 2. Methods 13 3. Results 21 4. Discussion 31 Chapter 3. Experiment 2 34 1. Objective and Hypotheses 34 2. Methods 35 3. Results 38 4. Discussion 50 Chapter 4. Conclusion 52 Chapter 5. General Discussion 54 References 60 Appendix 70 국문초록 77Maste

Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body