A Case Study on the Acceptance of a Humanoid Robotic Head Employed in Three Public Spaces
2026-07-27 • Robotics
RoboticsArtificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors studied how people interact with a human-like robot that can understand and respond in multiple languages with emotions. They placed the robot in three public places for several days and asked visitors to talk to it. People generally found the robot useful and easy to use, especially in places like tourist info centers and libraries, but less so in office settings. Some users felt the robot responded too slowly, which made conversations harder. The authors suggest improving response times for better interaction.
human-like robotnatural language processingmultimodal responseemotion simulationpublic interactionTAM2 questionnairemulti-lingual responseuser acceptanceresponse timedialog flow
Authors
Marcel Heisler, Luca Randecker, Christian Becker-Asano
Abstract
Previous research has shown that a human-like robot's acceptance heavily depends on the setting in which it operates and its ability to perform relevant tasks. This paper, first, reports on how our robot processes natural language to generate a multimodal, verbal response integrating emotional expressions based on an emotion simulation backend. Then, it describes how visitors were invited to speak with our robot in their own language at three different, public locations, where the robot was running continuously for several days. The TAM2 questionnaire results reveal that on average users were motivated to use the robot and found it rather useful and easy to use regardless of the specific location. However, public spaces like the tourist information and the city library seem to be a better fit for our interactive, robotic head than an office environment such as the building authority, where the willingness to interact was lower. Overall, the robot's multi-lingual responses were very much appreciated, but every fifth user found the response time too slow impeding the dialog flow, which remains to be improved in future work.