DotTip: Enhancing Dexterous Robotic Manipulation With a Tactile Fingertip Featuring Curved Perceptual Morphology

Abstract

Tactile sensing technologies enable robots to interact with the environment in increasingly nuanced and dexterous ways. A significant gap in this domain is the absence of curved tactile sensors, which are essential for performing sophisticated manipulation tasks. In this study, we present DotTip, a tactile fingertip featuring a three-dimensional curved perceptual surface that closely mimics human fingertip morphology. A convolutional neural network-based deep learning framework precisely calculates the contact angles and forces from the sensor tactile images, achieving mean errors of 1.56<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> and 0.28 N, respectively. DotTip's performance is evaluated in real-world tasks, demonstrating its efficacy in tactile servoing, slip prevention, and grasping, along with the more challenging benchmark task of controlling a joystick. These findings demonstrate that DotTip possesses superior 3D tactile sensing capabilities necessary for fine-grained dexterous manipulations compared to its flat counterparts.