Estimating Deformation of Tubular Object from WiFi CSI Toward Tactile Sensing

This study explores Wi-Fi Channel State Information (CSI) as a novel tactile sensing approach to overcome the limitations of existing methods. Tactile sensing, particularly for detecting object deformation due to human touch, is crucial in human-robot interaction, especially with the rise of collaborative robots and assistive devices. Traditional tactile sensors are effective but face challenges like complex wiring, fragility, and the risk of electric leakage. Vision-based sensing provides accurate deformation detection but is hindered by visibility constraints and high costs. Our research leverages CSI obtained from WiFi signals propagating through an object to estimate its deformation. CSI sensing is an emerging integrated sensing and communication (ISAC) technique that captures environmental changes via wireless signal propagation, offering a cost-effective alternative. We introduce a machine-learning model trained to predict object shape from CSI data, validated against a camera-based shape sensor. Experimental results using IEEE 802.11n WiFi devices, ESP32, and an aluminum pipe demonstrate that our CSI-based approach can accurately predict object deformation, suggesting it as a promising alternative to conventional methods. This research opens new possibilities for tactile sensing in soft robotics and broadens the application range of CSI sensing.