Automatic ontology creation, aiming to develop ontology without or with minimal human intervention, is needed for robots that work in dynamic environments. This is particularly required for service (or domestic) robots that work in unstructured and dynamic domestic environments, as robots and their human users share the same space. Most current works adopt learning to build the ontology in terms of defining concepts and relations of concepts, from various data and information resources. Given the partial or incomplete information often observed by robots in domestic environments, identifying useful data and information and extracting concepts and relations is challenging. In addition, more types of relations which do not appear in current approaches for service robots such as “HasA” and “MadeOf”, as well as semantic knowledge, are needed for domestic robots to cope with uncertainties during human–robot interaction. This research has developed a framework, called Data-Information Retrieval based Automated Ontology Framework (DIRAOF), that is able to identify the useful data and information, to define concepts according to the data and information collected, to define the “is-a” relation, “HasA” relation and “MadeOf” relation, which are not seen in other works, to evaluate the concepts and relations. The framework is also able to develop semantic knowledge in terms of location and time for robots, and a recency and frequency based algorithm that uses the semantic knowledge to locate objects in domestic environments. Experimental results show that the robots are able to create ontology components with correctness of 86.5% from 200 random object names and to associate semantic knowledge of physical objects by presenting tracking instances. The DIRAOF framework is able to build up an ontology for domestic robots without human intervention.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第17926号 / 情博第508号 / 新制||情||90(附属図書館) / 30746 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 奥乃 博, 教授 河原 達也, 教授 中村 裕一, 教授 五十嵐 淳 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Service robots are an emerging technological advancement increasingly utilized in the hospitality industry. In return, service workers are required to use them during their interactions with customers. This study sought to examine how the service robot-worker team affects cognitive and affective empathy present in a service encounter. Using the empathy attribute part of the SERVQUAL model, this study aimed to examine and differentiate the server's empathy impact in the presence of a service robot. This study utilized semi-structured qualitative interviews to collect data from service workers employed in two restaurants characterized by distinct service environments. The interviews were qualitatively analyzed through identifying common patterns and emergent themes. The findings exhibit how cognitive and affective empathy was perceived differently amongst service robot-worker teams and provides implications on the implementation of well-being practices for workers and further service robot collaboration.

Symbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms.

Although much progress has been made in the field of robotic mapping, many challenges remain including: efficient semantic segmentation using RGB-D sensors, map representations that include complex features (structures and objects), and interfaces for interactive annotation of maps. This thesis addresses how prior knowledge of semi-structured human environments can be leveraged to improve segmentation, mapping, and semantic annotation of maps. We present an organized connected component approach for segmenting RGB-D data into planes and clusters. These segments serve as input to our mapping approach that utilizes them as planar landmarks and object landmarks for Simultaneous Localization and Mapping (SLAM), providing necessary information for service robot tasks and improving data association and loop closure. These features are meaningful to humans, enabling annotation of mapped features to establish common ground and simplifying tasking. A modular, open-source software framework, the OmniMapper, is also presented that allows a number of different sensors and features to be combined to generate a combined map representation, and enabling easy addition of new feature types.

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<p>  </p> <p>With robot utilization reaching $300 million in the hospitality industry, this paper aims to examine the difference in customer response between two types of anthropomorphic features (cute vs. cool) of service robots. Four scenario-based experiments (2 [robot appearances: cute vs. cool] x 2 [customer–company relationship norms: communal vs. exchange]) were employed in two different contexts (Study 1: service successful and Study 2: failure). The results showed that cute robots elicit higher customer satisfaction, repatronage intention, and willingness to spread positive word of mouth when customers were in a communal relationship with a company. The difference was significant only in the situation in which the robots’ service failed. This study offers the industry guidelines to decide on robot design according to their relationship with the customer and develops the topic of anthropomorphism in robots in that it looked into the different traits within anthropomorphism rather than human likeness versus nonhuman likeness.</p>

The retail sector is experiencing transforming changes driven by service robots (SR). While SR promise benefits such as improved inventory management, increased efficiency, and enhanced customer satisfaction, their integration presents technical and societal challenges. Technical hurdles encompass image classification under varying conditions, data security, localization, and obstacle detection. On the societal front, concerns revolve around public perception, safety, and employee apprehension regarding job security. This paper provides a comprehensive overview of these challenges and opportunities, highlighting that while SR have considerable potential, their optimal utilization in retail may be best suited for non-human facing tasks, given the current limitations of AI in human interactions.

There are many aspects of human communication that affects the nature of an interaction; examples include voice intonation and facial expressions. A particular type of verbal and non-verbal cues, so called backchannels, have an underlying role in shaping conversations. In this study we analyse how backchannels can affect engagement of two participants engaged in a task with a social robot. Furthermore, given the ever increasing interest in using social robots in service contexts, we analyse the current level of customer acceptance for social robots and which aspects the participants think is important when interacting with one in a service setting using interviews. The social robot produces contingent backchannels based on engagement levels to increase the participation of the least speaking participant. An interview was conducted after the experiment to analyse the participants attitudes towards the use of social robots in services. 40 people participated in pairs of two, where each pair was assigned to either the experimental or the control condition. In the experimental condition the backchannels were targeted towards the least dominant speaker and in the control setting the backchannels were randomly generated. Each pair consisted of one native speaker and one language learner of Swedish. The results showed that in the experimental condition the least dominant speaker increased their speech, as well as evening out the participation. The interviews showed mixed attitudes towards the use of social robots in service with some expressing hesitancy regarding the robots ability to understand speaker’s desire. / Det finns många aspekter i kommunikation mellan människor som har inverkan över hur en konversation uppfattas, exempelvis röstläge och ansiktsuttryck. En samling av dessa verbala och icke-verbala signaler kallas för stödsignaler och fyller en viktig roll i konversationer. Denna rapport undersöker hur adaptiv generering av dessa signaler hos en social robot kan öka engagemang hos två deltagare som spelar ett språkinlärningsspel tillsammans med en denna robot. Givet ett allt större intresse för att tillämpa sociala robotar i en tjänstemiljö kommer denna rapport även undersöka nuvarande attityder gällande detta hos deltagarna. Spelet spelades på svenska och de två deltagarna bestod av en modersmålstalare och en icke-modersmålstalare. Efter experimenten utfördes en kort intervju för att undersöka deltagarnas attityd gällande användningen av sociala robotar i tjänstesammanhang. Totalt deltog 40 deltagare uppdelade i 20 olika par, där varje grupp antingen spelade spelet under det experimentella förhållandet, där s.k. stödsignaler var riktade mot den deltagare som talade minst, eller kontrollförhållandet där stödsignalerna genererades slumpmässigt. Resultaten visade på att i det experimentella förhållandet så ökade den totala taltiden hos den talare med lägre språkfärdighet jämfört med under kontrollförhållandet, samtidigt som konversationen också blev jämnare mellan de två deltagarna under det experimentella förhållandet. Intervjuerna visade också över lag en positiv attityd gentemot användning av robotar i tjänstemiljöer, dock rådde det en viss skepsis då vissa var oroliga inför att roboten inte skulle förstå vad man sade.

Artificial intelligence (AI) and robotics are revolutionizing the traditional paradigm of business operations and transforming consumers’ experiences by promoting human–robot interaction in tourism and hospitality. Nonetheless, research related to customers’ experiences with robot-related services in this industry remains scant. This study thus seeks to investigate hotel customers’ experiences with service robots and how robot-based experiences shape customers’ satisfaction with hotel stays. Specifically, three research questions are addressed: (a) What are hotel customers’ primary concerns about robots and robot-related services? (b) Do hotel customers’ experiences with robotic services shape guests’ overall satisfaction? (c) How do service robots’ attributes affect guests’ forgiveness of robots’ service failure? This dissertation consists of three chapters. Chapter 1 introduces the overall research background. Chapter 2 answers the first two research questions by combining text mining and regression analyses; Chapter 3 addresses the third question by introducing social cognition into this investigation and performing an experiment. Overall, sentiment analyses uncovered customers’ generally positive experiences with robot services. Machine learning via latent Dirichlet allocation modeling revealed three key topics underlying hotel guests’ robot-related reviews—robots’ room delivery services, entertainment and catering services, and front office services. Regression analyses demonstrated that hotel robots’ attributes (e.g., mechanical vs. AI-assistant robots) and robot reviews’ characteristics (e.g., sentiment scores) can influence customers’ overall satisfaction with hotels. Finally, the experimental study verified uncanny valley theory and the existence of social cognition related to service robots (i.e., warmth and competence) by pointing out the interactive effects of robots’ anthropomorphism in terms of their facial expressions, voices, and physical appearance. These findings collectively yield a set of theoretical implications for researchers along with practical implications for hotels and robot developers. / Tourism and Sport