Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access and may raise privacy concerns, for example, in nursing facilities. This research pioneers an innovative approach harnessing channel state information (CSI) measured from WiFi signals, subtly influenced by the activity of robotic arms. We developed an attention-based network to classify eight distinct activities performed by a Franka Emika robotic arm in different situations. Our proposed bidirectional vision transformer-concatenated (BiVTC) methodology aspires to predict robotic arm activities accurately, even when trained on activities with different velocities, all without dependency on external or internal sensors or visual aids. Considering the high dependency of CSI data to the environment, motivated us to study the problem of sniffer location selection, by systematically changing the sniffer's location and collecting different sets of data. Finally, this paper also marks the first publication of the CSI data of eight distinct robotic arm activities, collectively referred to as RoboFiSense. This initiative aims to provide a benchmark dataset and baselines to the research community, fostering advancements in the field of robotics sensing.

翻译：尽管当前自主机器人系统研究热潮涌动，但在受限室内环境中实现机器人活动识别仍是重大挑战。传统检测与识别机械臂活动的方法通常依赖视觉或激光雷达（LiDAR）传感器，这些传感器需视线（LoS）通达，且在养老设施等场景可能引发隐私问题。本研究开创性地利用WiFi信号中受机械臂活动微妙影响的信道状态信息（CSI），提出一种创新方法。我们开发了基于注意力机制的神经网络，用于分类Franka Emika机械臂在不同场景下执行的八种不同活动。所提出的双向视觉Transformer拼接（BiVTC）方法，即使在不同运动速度的训练数据下，也能在不依赖外部或内部传感器及视觉辅助的前提下准确预测机械臂活动。鉴于CSI数据对环境的强依赖性，我们通过系统改变嗅探器位置并采集多组数据，深入研究了嗅探器位置选择问题。此外，本文首次公开发布了涵盖八种机械臂活动的CSI数据集，统称为RoboFiSense。该举措旨在为研究社区提供基准数据集与基线方法，推动机器人感知领域的发展。