Contextual Non-verbal Behaviour Generation for Humanoid Robot Using Text Sentiment

This paper describes an approach to synthesise non-verbal behaviours for a humanoid robot Pepper using spoken text. Our approach takes into account the sentiment of the spoken text and maps the appropriate gesture and sound relevant to that text in a parameterised manner. This work forms a basis for our planned user study where we will evaluate this approach

Shaping Robot Gestures to Shape Users' Perception: the Effect of Amplitude and Speed on Godspeed Ratings

This work analyses the relationship between the way robots gesture and the way those gestures are perceived by human users. In particular, this work shows how modifying the amplitude and speed of a gesture affect the Godspeed scores given to those gestures, by means of an experiment involving 45 stimuli and 30 observers. The results suggest that shaping gestures aimed at manifesting the inner state of the robot (e.g., cheering or showing disappointment) tends to change the perception of Animacy (the dimension that accounts for how driven by endogenous factors the robot is perceived to be), while shaping gestures aimed at achieving an interaction effect (e.g., engaging and disengaging) tends to change the perception of Anthropomorphism, Likeability and Perceived Safety (the dimensions that account for the social aspects of the perception)

Do We Really Like Robots that Match our Personality? The Case of Big-Five Traits, Godspeed Scores and Robotic Gestures

This work investigates the role of the attraction paradigm — the tendency to associate similarity and attraction in interpersonal relations — in Human-Robot Interaction. The experiment presented here involved 30 human observers who watched and rated 45 robotic gestures in terms of BigFive personality traits and Godspeed scores. The results show that, for 24 of the 30 observers, there was a statistically significant correlation between the Godspeed scores and the perceived similarity between the robot’s personality and their own. However, the association was positive for 15 subjects — meaning that for these there is a similarity-attraction effect — and negative for the other 9 — meaning that for these there is a complementarity-attraction effect. Furthermore, the strength of the effect depends on the particular trait under examination

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

Persoonallisuuspiirteet ja luottamus robotteihin ja tekoälyyn

Robottien ja tekoälyn käyttö on lisääntynyt merkittävästi viimeisten vuosikymmenten aikana eri aloilla. Niitä käytetään yhä enemmän tilanteissa, jotka ovat vaarallisia tai jostain muusta syystä ihmisten ulottumattomissa. Erilaiset pelastustehtävät, lääketieteelliset operaatiot, puolustusvoimat tai avaruuden tutkimus ovat vain pieni osa siitä, mihin kaikkeen robottien ja tekoälyn käyttö mahdollistaa. Tilanteissa, joissa ihmiset joutuvat tekemään yhteistyötä robottien kanssa, on luottamus keskeistä ja sekä liian korkea että liian matala luottamus voivat olla kohtalokkaita. Yksi luottamukseen vaikuttavista tekijöistä on ihmisten persoonallisuuspiirteet. Vaikka niiden merkitys luottamukseen vaikuttaa olevan kiistaton, yksittäisten piirteiden vaikutuksesta on aiemmissa tutkimuksissa saatu ristiriitaisia tuloksia. Tämän tutkimuksen tavoitteena oli tutkia persoonallisuuspiirteiden yhteyttä robotteja sekä tekoälyä kohtaan tunnettuun luottamukseen. Samalla tutkittiin iän, sukupuolen, työn ja koulutuksen yhteyttä robotteja ja tekoälyä kohtaan tunnettuun luottamukseen sekä sitä, miten luottamus robotteihin eroaa luottamuksesta tekoälyyn. Tutkimus toteutettiin osana Tampereen yliopiston monitieteistä Robotit ja me: vuorovaikutuksen fysiologinen, psykologinen ja sosiaalinen ulottuvuus -hanketta. Luottamusta tutkittiin luottamuspelin avulla ja yhdysvaltalaisista koostuva aineisto (n = 969) kerättiin verkkokyselynä. Analyysiin käytettiin kuvailevia analyysejä ja lineaarista regressioanalyysiä. Tekoälyryhmässä avoimuuden ja iän havaittiin ennustavan positiivisesti ja tunnollisuuden negatiivisesti luottamusta. Robottiryhmässä ainoastaan avoimuus oli tilastollisesti merkitsevästi yhteydessä luottamuksen kanssa. Havaitut yhteydet eivät olleet voimakkaita, mutta ne olivat tilastollisesti merkitseviä. Osallistujien luottamus ei vaihdellut robottien ja tekoälyn välillä, ja molemmille annettiin suunnilleen yhtä paljon rahaa. Tässä tutkimuksessa saadut tulokset vastaavat osittain aiemmin tehtyjä tutkimuksia. Kirjallisuudessa tulokset ovat kuitenkin paikoin hyvinkin ristiriitaisia ja lisää tutkimuksia tarvitaan etenkin persoonallisuuspiirteiden ja luottamuksen välillä, jotta saataisiin kehitettyä luotettavampia robotteja ja tekoälyä, jotka olisi helpompi hyväksyä

人の行動計測によるユーザタイプを考慮したヒューマンロボットインタラクション

首都大学東