  We introduce DIFFTACTILE, a physics-based differentiable tactile simulation
system designed to enhance robotic manipulation with dense and physically
accurate tactile feedback. In contrast to prior tactile simulators which
primarily focus on manipulating rigid bodies and often rely on simplified
approximations to model stress and deformations of materials in contact,
DIFFTACTILE emphasizes physics-based contact modeling with high fidelity,
supporting simulations of diverse contact modes and interactions with objects
possessing a wide range of material properties. Our system incorporates several
key components, including a Finite Element Method (FEM)-based soft body model
for simulating the sensing elastomer, a multi-material simulator for modeling
diverse object types (such as elastic, elastoplastic, cables) under
manipulation, a penalty-based contact model for handling contact dynamics. The
differentiable nature of our system facilitates gradient-based optimization for
both 1) refining physical properties in simulation using real-world data, hence
narrowing the sim-to-real gap and 2) efficient learning of tactile-assisted
grasping and contact-rich manipulation skills. Additionally, we introduce a
method to infer the optical response of our tactile sensor to contact using an
efficient pixel-based neural module. We anticipate that DIFFTACTILE will serve
as a useful platform for studying contact-rich manipulations, leveraging the
benefits of dense tactile feedback and differentiable physics. Code and
supplementary materials are available at the project website
https://difftactile.github.io/.
