Papers › RoboFiSense: Attention-Based Robotic Arm Activity Recognition with WiFi Sensing
RoboFiSense: Attention-Based Robotic Arm Activity Recognition with WiFi Sensing
Rojin Zandi, Kian Behzad, Elaheh Motamedi, Hojjat Salehinejad, Milad Siami
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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 on 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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections