Reitelshöfer S, Merz N, Garcia Gamarra GM, Wei Y, Franke J (2025)
Publication Language: English
Publication Type: Journal article
Publication year: 2025
Book Volume: 12
Article Number: 1534346
DOI: 10.3389/frobt.2025.1534346
The increasing integration of autonomous robotic systems across various industries necessitates adaptable social interaction capabilities. This paper presents a novel software architecture for socially adaptable robots, emphasizing simplicity, domain independence, and user influence on robotic behaviour. The architecture leverages a marketplace-based agent selection system to dynamically adapt social interaction patterns to diverse users and scenarios. Implemented using ROS2, the framework comprises four core components: scene analysis, a bidding platform, social agents, and a feedback service. A Validation through simulated experiments shows the architecture’s feasibility and adaptability, with respect to varying feedback conditions and learning rates. This work lays the foundation for scalable, adaptable, and user-friendly robotic systems, addressing key challenges in industrial and social robotics. Future improvements include enhanced scene analysis, integration of machine learning techniques, and support for more complex behavioural scripts.
APA:
Reitelshöfer, S., Merz, N., Garcia Gamarra, G.M., Wei, Y., & Franke, J. (2025). Making social robots adaptable and to some extent educable by a marketplace for the selection and adjustment of different interaction characters living inside a single robot. Frontiers in Robotics and AI, 12. https://doi.org/10.3389/frobt.2025.1534346
MLA:
Reitelshöfer, Sebastian, et al. "Making social robots adaptable and to some extent educable by a marketplace for the selection and adjustment of different interaction characters living inside a single robot." Frontiers in Robotics and AI 12 (2025).
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