GradTac: Spatio-Temporal Gradient Based Tactile Sensing

TL;DR

GradTac introduces a spatio-temporal gradient method for fluid-based tactile sensors, improving noise robustness and accuracy in tactile contour tracking, force measurement, and slip detection on robotic hands.

Contribution

It presents a novel algorithm that converts tactile sensor data into spatio-temporal surfaces, enhancing robustness and accuracy over raw data, and provides a new dataset for tactile sensing research.

Findings

Effective tactile contour tracking demonstrated on real-world experiments.

Improved noise robustness in tactile measurements.

Successful detection of slip and edge tracking.

Abstract

Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck the right balance of anthropomorphic sizes and shapes and accuracy of tactile response measurement. However, this design is plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing mechanism "damping" the measurement values that are hard to model. To this end, we present a spatio-temporal gradient representation on the data obtained from fluid-based tactile sensors, which is inspired from neuromorphic principles of event based sensing. We present a novel algorithm (GradTac) that converts discrete data points from spatial tactile sensors into spatio-temporal surfaces and tracks tactile contours across these surfaces. Processing the tactile data using the proposed spatio-temporal domain is robust, makes it less susceptible to the inherent noise from the fluid based sensors, and allows accurate tracking of regions of touch as compared to using the raw data. We successfully evaluate and demonstrate the efficacy of GradTac on many real-world experiments performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors. Specifically, we use it for tracking tactile input across the sensor's surface, measuring relative forces, detecting linear and rotational slip, and for edge tracking. We also release an accompanying task-agnostic dataset for the BioTac SP, which we hope will provide a resource to compare and quantify various novel approaches, and motivate further research.