User Perception on Personalized Explanation by Science Museum Docent Robot

Park, Jeyoung; Kim, Jeeyeon; Kim, Da‐Young; Kim, Ju-Hyun; Kim, Min; Choi, JiHwan; Lee, WonHyong

doi:10.1109/hri53351.2022.9889654

Cited by 4 publications

(1 citation statement)

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“…Multiple works [34] proposed technical methods to integrate social navigation, user perception, and verbal and non-verbal communication in order to adopt different behaviors on the basis of the users involved in the interaction. Recent works [35][36][37] tried to implement a much more effective and personalized user experience by designing robots that are able to exploit their perceptive and decision-making skills to establish much deeper interactions with visitors. Our approach is to try to achieve a real user's adaptation, where the robot engages in reasoning based on the task delegated by the user.…”

Section: Adaptive Social Robots In Museum Settingsmentioning

confidence: 99%

Redefining User Expectations: The Impact of Adjustable Social Autonomy in Human–Robot Interaction

Cantucci,

Falcone,

Marini

2023

Electronics

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To promote the acceptance of robots in society, it is crucial to design systems exhibiting adaptive behavior. This is particularly needed in various social domains (e.g., cultural heritage, healthcare, education). Despite significant advancements in adaptability within Human-Robot Interaction and Social Robotics, research in these fields has overlooked the essential task of analyzing the robot’s cognitive processes and their implications for intelligent interaction (e.g., adaptive behavior, personalization). This study investigates human users’ satisfaction when interacting with a robot whose decision-making process is guided by a computational cognitive model integrating the principles of adjustable social autonomy. We designed a within-subjects experimental study in the domain of Cultural Heritage, where users (e.g., museum visitors) interacted with the humanoid robot Nao. The robot’s task was to provide the user with a museum exhibition to visit. The robot adopted the delegated task by exerting some degree of discretion, which required different levels of autonomy in the task adoption, relying on its capability to have a theory of mind. The results indicated that as the robot’s level of autonomy in task adoption increased, user satisfaction with the robot decreased, whereas their satisfaction with the tour itself improved. Results highlight the potential of adjustable social autonomy as a paradigm for developing autonomous adaptive social robots that can improve user experiences in multiple HRI real domains.

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