Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing

--Scaling tactile sensing for robust whole-body manipulation is a significant challenge, often limited by wiring complexity, data throughput, and system reliability. This paper presents a complete architecture designed to overcome these barriers. Our approach pairs open-source, fabric-based sensors with custom readout electronics that reduce signal crosstalk to less than 3.3% through hardware-based mitigation. Critically, we introduce a novel, daisy-chained SPI bus topology that avoids the practical limitations of common wireless protocols and the prohibitive wiring complexity of USB hub-based systems. We validate the system's efficacy in a whole-body grasping task where, without feedback, the robot's open-loop trajectory results in an uncontrolled application of force that slowly crushes a deformable cardboard box. With real-time tactile feedback, the robot transforms this motion into a gentle, stable grasp, successfully manipulating the object without causing structural damage. This work provides a robust and well-characterized platform to enable future research in advanced whole-body control and physical human-robot interaction. ESEARCH in robotic manipulation is driven by a desire to enhance the capabilities of robots operating in inherently unstructured environments and manipulating objects of infinite variability. While vision is a powerful modality for robotic manipulation [1], its utility degrades when objects are occluded or when tasks require more dexterous, force-sensitive interactions. Adding more cameras can mitigate occlusion but does not scale well for complex, open-world scenarios [2]. In contrast, tactile sensing provides critical information about contact forces, local geometry, textures, and slip that is difficult or impossible to obtain with vision alone, much like haptic feedback improves human manipulation [3], [4]. Historically, robotic tactile sensing has been concentrated at the end-effector, analogous to the human fingertip.