9DTact: A Compact Vision-Based Tactile Sensor for Accurate 3D Shape Reconstruction and Generalizable 6D Force Estimation

TL;DR

9DTact is a compact, low-cost vision-based tactile sensor capable of accurate 3D shape reconstruction and generalizable 6D force estimation, enhancing robotic contact-rich manipulation.

Contribution

This work introduces 9DTact, a versatile tactile sensor that combines optical principles with deformable gel to achieve high-accuracy shape and force sensing without auxiliary markers.

Findings

Achieves improved 3D shape reconstruction accuracy.

Successfully estimates 6D forces on unseen objects.

Open-sourced hardware and software for community use.

Abstract

The advancements in vision-based tactile sensors have boosted the aptitude of robots to perform contact-rich manipulation, particularly when precise positioning and contact state of the manipulated objects are crucial for successful execution. In this work, we present 9DTact, a straightforward yet versatile tactile sensor that offers 3D shape reconstruction and 6D force estimation capabilities. Conceptually, 9DTact is designed to be highly compact, robust, and adaptable to various robotic platforms. Moreover, it is low-cost and easy-to-fabricate, requiring minimal assembly skills. Functionally, 9DTact builds upon the optical principles of DTact and is optimized to achieve 3D shape reconstruction with enhanced accuracy and efficiency. Remarkably, we leverage the optical and deformable properties of the translucent gel so that 9DTact can perform 6D force estimation without the participation of auxiliary markers or patterns on the gel surface. More specifically, we collect a dataset consisting of approximately 100,000 image-force pairs from 175 complex objects and train a neural network to regress the 6D force, which can generalize to unseen objects. To promote the development and applications of vision-based tactile sensors, we open-source both the hardware and software of 9DTact, along with a comprehensive video tutorial, all of which are available at https://linchangyi1.github.io/9DTact.