Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the same jobsite creates a unique environmental context. Overlooking contextual information would limit the potential to optimize interaction efficiency. This paper proposes a novel context‐aware method that utilizes a two‐stream network to enhance human–robot interaction in construction settings. In the proposed network, the first‐person view‐based stream focuses on the relevant spatiotemporal regions for context extraction, while the motion sensory data‐based stream obtains features related to hand motions. By fusing the vision context and motion data, the method achieves gesture recognition for efficient communication between construction workers and robots. Experimental evaluation on a dataset from five construction sites demonstrates an overall classification accuracy of 92.6%, underscoring the practicality and potential benefits of the proposed method.

中文翻译：

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用于建筑中人机协作的情境感知手势交互

建筑机器人在建筑行业的智能流程中发挥着关键作用。促进高效人机协作的用户友好界面对于促进机器人的采用至关重要。然而，大多数现有界面没有考虑协作环境中的上下文信息。人类和机器人在同一工地一起工作的情况创造了独特的环境背景。忽视上下文信息会限制优化交互效率的潜力。本文提出了一种新颖的上下文感知方法，利用双流网络来增强建筑环境中的人机交互。在所提出的网络中，基于第一人称视图的流侧重于上下文提取的相关时空区域，而基于运动感知数据的流则获得与手部运动相关的特征。通过融合视觉上下文和运动数据，该方法实现了手势识别，从而实现建筑工人和机器人之间的高效通信。对来自五个建筑工地的数据集的实验评估表明，总体分类精度为 92.6%，强调了该方法的实用性和潜在好处。