  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.
