[2603.00016] Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training

Computer Science > Robotics arXiv:2603.00016 (cs) [Submitted on 31 Jan 2026] Title:Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training Authors:Nicolas Leins, Jana Gonnermann-Müller, Malte Teichmann, Sebastian Pokutta View a PDF of the paper titled Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training, by Nicolas Leins and 2 other authors View PDF HTML (experimental) Abstract:Augmented Reality (AR) offers powerful visualization capabilities for industrial robot training, yet current interfaces remain predominantly static, failing to account for learners' diverse cognitive profiles. In this paper, we present an AR application for robot training and propose a multi-agent AI framework for future integration that bridges the gap between static visualization and pedagogical intelligence. We report on the evaluation of the baseline AR interface with 36 participants performing a robotic pick-and-place task. While overall usability was high, notable disparities in task duration and learner characteristics highlighted the necessity for dynamic adaptation. To address this, we propose a multi-agent framework that orchestrates multiple components to perform complex preprocessing of multimodal inputs (e.g., voice, physiology, robot data) and adapt the AR application to the learner's needs. By utilizing autonomous Large Language Model (LLM) agents, the proposed system would dynamically adapt the...