Toward User-Mediated Self-Repair in Ubiquitous Robots Through Goal-Oriented Agentic AI

Abstract:Ubiquitous robotic systems often lack traditional visual interfaces, necessitating resilient natural language interaction for maintenance and repair tasks. This paper presents a goal oriented agentic AI architecture designed to enable non-expert users to perform technical repairs through situated dialogue. The framework utilizes a multi-layered approach that decouples high-level strategic planning from reactive conversational execution to transform unconstrained human instructions into a structured hierarchy of goals. We conducted a study involving twenty participants to evaluate the system's efficacy using a physical hardware testbed. The architecture achieved a 95\% task completion rate, and participants reported positive self-efficacy following real-time guidance that adapted to conversational diversions and linguistic variations. A comparative analysis with an online baseline revealed that the transition to a physical environment significantly decreased perceived social presence (p=.0005), and trust and competence, (p=.037), while the agentic framework remained robust throughout the interaction. These findings indicate that goal oriented agentic AI can support the sustainability of body-worn technologies by empowering users to perform critical maintenance in ubiquitous contexts.

Artificial Intelligence (cs.AI); Robotics (cs.RO)

arXiv:2609.26157 [cs.AI]

(or arXiv:2609.26157v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.26157 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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