Self-driving laboratories (SDLs) are rapidly transforming research in chemistry and materials science to accelerate new discoveries. Mobile robot chemists (MRCs) play a pivotal role by autonomously navigating the lab to transport samples, effectively connecting synthesis, analysis, and characterisation equipment. The instruments within an SDL are typically designed or retrofitted to be accessed by both human and robotic chemists, ensuring operational flexibility and integration between manual and automated workflows. In many scenarios, human and robotic chemists may need to use the same equipment simultaneously. Currently, MRCs rely on simple LiDAR-based obstruction detection, which forces the robot to passively wait if a human is present. This lack of situational awareness leads to unnecessary delays and inefficient coordination in time-critical automated workflows in human-robot shared labs. To address this, we present an initial study of an embodied, AI-driven perception method that facilitates proactive human-robot interaction in shared-access scenarios. Our method features a hierarchical human intention prediction model that allows the robot to distinguish between preparatory actions (waiting) and transient interactions (accessing the instrument). Our results demonstrate that the proposed approach enhances efficiency by enabling proactive human-robot interaction, streamlining coordination, and potentially increasing the efficiency of autonomous scientific labs.

翻译：自主实验室（SDL）正在迅速改变化学与材料科学的研究范式，以加速新发现。移动机器人化学家（MRC）在其中扮演关键角色，通过自主导航实验室运输样品，有效连接合成、分析与表征设备。SDL中的仪器通常经过专门设计或改造，可供人类与机器人化学家共同使用，从而确保操作灵活性及人工与自动化工作流程的集成。在许多场景中，人类与机器人化学家可能需要同时使用同一设备。目前，MRC主要依赖基于LiDAR的简单障碍物检测技术，当检测到人类存在时，机器人只能被动等待。这种情境感知能力的缺失，导致在人机共享实验室中时间敏感的自动化工作流程产生不必要的延迟与低效协调。为解决这一问题，我们提出了一项具身化AI驱动感知方法的初步研究，该方法可促进共享访问场景中主动式人机交互。我们的方法采用分层式人类意图预测模型，使机器人能够区分预备性动作（等待）与瞬时交互（使用仪器）。实验结果表明，所提出的方法通过实现主动式人机交互、优化协调流程，显著提升了系统效率，并有望增强自主科学实验室的整体运行效能。