While non-verbal behaviors and expressive movements are essential for natural human-robot interaction, existing methods often overlook a crucial element: the human's internal cognitive state. Frequently, proactive multi-agent systems can interrupt humans at inopportune moments, leading to cognitive overload and decreased task performance. This paper introduces a framework for generating "cognitively aligned" multi-agent interactions, enhancing the ability of robotic systems to contextually defer communications to the user of an agent system during moments of high human mental workload and engagement. We present the design and implementation of a closed-loop architecture that explores the interplay between autonomous task execution and real-time neurophysiological focus. Using a consumer-grade Brain-Computer Interface (BCI), our approach continuously monitors Electroencephalography (EEG) spectral band powers while a human performs an engagement-inducing task. We propose an engagement-driven pipeline where an HTTP-based signaling mechanism places a primary agent's sensory inputs and audio outputs into a holding state upon detecting high engagement. This allows secondary agents to seamlessly process complex, delegated tasks in the background. Once the human's cognitive state returns to a lower cognitive load baseline, the primary agent releases the queued agent message. Our preliminary results demonstrate the feasibility of leveraging real-time signal processing, Large Language Models (LLMs), and physical robotic embodiments to create cognitively-aware, non-intrusive multi-agent systems.

翻译：尽管非语言行为和表达性动作对自然的人机交互至关重要，现有方法常忽略一个关键要素：人类的内部认知状态。频繁的主动多智能体系统可能在不当的时机中断人类，导致认知超负荷和任务绩效下降。本文提出一个生成"认知对齐"多智能体交互的框架，增强机器人系统在高人类心智负荷与专注状态下，将通信向智能体系统用户进行情境化延迟的能力。我们展示一种闭环架构的设计与实现，探索自主任务执行与实时神经生理学焦点之间的相互作用。通过使用消费级脑机接口（BCI），本研究方法在人类执行引发专注的任务时，持续监测脑电图（EEG）谱带功率。我们提出一种专注驱动流水线，其中基于HTTP的信令机制在检测到高专注度时，将主智能体的感官输入和音频输出置于保持状态。这使得次级智能体可在后台无缝处理复杂的委派任务。一旦人类认知状态恢复至较低认知负荷基线，主智能体释放队列中的智能体消息。初步结果证明了利用实时信号处理、大语言模型（LLMs）及物理机器人具身化，构建认知感知、非侵入式多智能体系统的可行性。