Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training
This addresses the need for adaptive training systems for industrial learners, but it is incremental as it builds on existing AR and AI methods without demonstrating a fully implemented solution.
The paper tackles the problem of static AR interfaces in industrial robot training by proposing a multi-agent AI framework for dynamic adaptation, reporting high usability but notable disparities in task duration and learner characteristics from a 36-participant evaluation.
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 learning environment based on advanced LLM reasoning in real-time.