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

arXiv: https://arxiv.org/abs/2609.26157 · HTML: https://mlsanity.com/paper/2609.26157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop](https://mlsanity.com/paper/2011.05533/) (2020)
- [RealityTalk: Real-Time Speech-Driven Augmented Presentation for AR Live Storytelling](https://mlsanity.com/paper/2208.06350/) (2022)
- [Robot Vulnerability and the Elicitation of User Empathy](https://mlsanity.com/paper/2401.02684/) (2024)
- [Improving Human-Robot Teaching by Quantifying and Reducing Mental Model Mismatch](https://mlsanity.com/paper/2501.04755/) (2025)
- [Toward Anxiety-Reducing Pocket Robots for Children](https://mlsanity.com/paper/2503.24041/) (2025)

## Abstract (arXiv)

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.