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/.

翻译：我们提出DIFFTACTILE，一种基于物理的可微分触觉模拟系统，旨在通过密集且物理精确的触觉反馈增强机器人操作能力。与以往主要关注刚体操控、且通常依赖简化近似来模拟接触中材料应力与变形的触觉模拟器不同，DIFFTACTILE强调高保真度的基于物理的接触建模，支持多种接触模式以及与具有广泛材料特性物体的交互。我们的系统包含几个关键组件：基于有限元方法（FEM）的软体模型用于模拟传感弹性体，用于模拟操作中多种物体类型（如弹性体、弹塑性体、线缆等）的多材料模拟器，以及用于处理接触动力学的惩罚式接触模型。系统的可微分特性促进了基于梯度的优化，这可用于：1）利用真实数据细化模拟中的物理属性，从而缩小模拟到现实的差距；2）高效学习触觉辅助抓取与接触丰富的操作技能。此外，我们引入了一种方法，通过高效的基于像素的神经模块推断触觉传感器对接触的光学响应。我们期望DIFFTACTILE能够作为一个有用的平台，借助密集触觉反馈与可微分物理的优势来研究接触丰富的操作。相关代码与补充材料可在项目网站https://difftactile.github.io/上获取。