用多智能体AI让AR机器人训练自动适应学习者差异。
Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training
- 多智能体系统实时处理语音、生理等多模态数据
- 36人测试显示任务时长差异显著，需动态调整
- 基于大模型推理，实现个性化自适应教学
增强现实（AR）为工业机器人培训提供强大可视化支持，但现有界面仍以静态为主，未能考虑学习者认知差异。本文提出一种面向机器人培训的AR应用，并设计了一个多智能体AI框架，旨在弥合静态可视化与教学智能之间的差距。通过36名参与者完成抓放任务的评估，结果显示整体可用性高，但任务时长和学习者特征存在显著差异，凸显动态适配的必要性。为此，我们提出一个由多个智能体协同工作的框架，可对多模态输入（如语音、生理信号、机器人数据）进行复杂预处理，并根据学习者需求动态调整AR应用。该系统利用自主的大语言模型（LLM）智能体，在实时推理中实现学习环境的个性化适应。
原文摘要 · Abstract (English)
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.
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