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

作者：Haoran Zheng, Xiaohang Shi, Ange Bao, Yongbin Jin, Pei Zhao · 发表于：IEEE Robotics and Automation Letters · 年份：2024 · DOI：10.1109/lra.2024.3511431 · 被引用次数：6 · 研究领域：Tactile and Sensory Interactions、Robot Manipulation and Learning、Modular Robots and Swarm Intelligence

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$^{\circ }$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.