  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.

Robotics;Signal Processing