Toward Self-Repairing Ubiquitous Robots Using Goal-Oriented Agentic AI in Human-Robot Interactions

Abstract:Ubiquitous robotic systems often lack traditional visual interfaces, making natural language interaction important for maintenance and repair. This paper presents a goal-oriented agentic AI architecture that enables non-expert users to complete technical repair tasks through situated dialogue. The architecture separates pre-interaction goal decomposition, persistent state tracking, strategic goal management, and real-time conversational execution. We evaluated the system in a physical hardware repair task with twenty participants. Nineteen participants completed the task, corresponding to a 95% completion rate. Participants rated the system as helpful and competent, and the agent remained robust to conversational diversions such as meta-queries and code-switching. A comparison with a prior online baseline showed that physical interaction significantly reduced perceived social presence, (p=.0005), and trust and competence, (p=.037), while perceived helpfulness remained high.

Robotics (cs.RO)

arXiv:2609.26155 [cs.RO]

(or arXiv:2609.26155v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.26155 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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