Xmobot: Enabling Rapid Build-and-Train Robotics Education With Agentic AI

Abstract

Robotics education increasingly requires learners to connect design choices to measurable task outcomes under realistic engineering constraints. In practice, however, educational robotics laboratories often face limited reusability of build-and-train workflows when robot configurations or task contexts change, while deep reinforcement learning (DRL) further raises the barrier through unstable training dynamics and difficult-to-interpret process traces. This article presents Xmobot, a configurable mobile robot platform and reusable build-and-train workflow for educational robotics, together with a tool-grounded agentic learning-support system for iterative DRL development. The proposed system retrieves standardized evidence from training logs, assesses run quality through an interpretable fuzzy-logic model, uses a constrained large language model (LLM) to generate structured experiment plans and learner-facing explanations, and applies deterministic constraint validation to ensure traceable and recoverable reward-design iteration. It also provides instructor-facing analytics that aggregate run-level evidence and intervention histories into compact diagnostic views for monitoring and feedback. In an IsaacLab mobile navigation study over a fixed ten-run horizon, the full tool-grounded system achieved zero constraint violations, a valid-plan rate of 1.000, reached the predefined task-success criterion by run 9, and obtained the highest final success rate (0.659), outperforming Manual (0.339), LLM-Chat (0.277), C-noFuzzy (0.370), and C-noValidate (0.612) in this experimental setting. These results suggest that the proposed framework supports more dependable and interpretable DRL iteration while also making robotics education more actionable for learner reflection, instructor monitoring, and formative feedback.