APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs

Abstract:Programming robots remains challenging for non-experts, as traditional methods require expert knowledge and even block-based interfaces often lack flexibility. Recent work has explored Large Language Models (LLMs) to automatically generate robot programs from natural language, but these systems remain limited by a lack of transparency, missing mechanisms to ensure alignment with user intent, and little support for reuse. We present APPROVE (AI-Powered Programming for Robots with Visual End-User Feedback), an LLM-based multi-modal end-user programming framework that integrates natural language input with a block-based interface and an explicit user confirmation step. Generated programs are visualized using a block-based interface in Blockly, allowing users to confirm, modify, or reject them before execution. Confirmed functions are stored in a library for reuse, gradually building a set of reliable program components. Our approach contributes a human-centered design for LLM-based robot programming that emphasizes user trust, intent alignment, and reusability.

Robotics (cs.RO); Human-Computer Interaction (cs.HC)

arXiv:2608.19281 [cs.RO]

(or arXiv:2608.19281v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.19281 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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