arXiv 2403.087162024-03-13cs.RO

DIFFTACTILE：用于接触丰富机器人操作的基于物理的可微分触觉模拟器

DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation

CMU RI（卡内基梅隆大学机器人研究所）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；UMass Amherst（马萨诸塞大学阿默斯特分校）

Zilin Si, Gu Zhang, Qingwei Ben, Branden Romero, Zhou Xian, Chao Liu, Chuang Gan

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AI总结：

DIFFTACTILE是一个基于物理的可微分触觉模拟器，通过高保真接触建模和梯度优化，支持多种材料与接触模式，用于缩小模拟到现实的差距并高效学习触觉辅助操作技能。

摘要：

我们介绍了DIFFTACTILE，一个基于物理的可微分触觉模拟系统，旨在通过密集且物理准确的触觉反馈增强机器人操作。与先前主要关注刚性体操作且通常依赖简化近似来模拟接触中材料应力和变形的触觉模拟器不同，DIFFTACTILE强调基于物理的高保真接触建模，支持多种接触模式以及与具有广泛材料属性的物体的交互模拟。我们的系统包含几个关键组件，包括基于有限元方法（FEM）的软体模型用于模拟传感弹性体，一个多材料模拟器用于模拟操作下的多种物体类型（如弹性、弹塑性、线缆），以及一个基于惩罚的接触模型用于处理接触动力学。我们系统的可微分特性便于基于梯度的优化，既用于1）利用真实世界数据细化模拟中的物理属性，从而缩小模拟到现实的差距，也用于2）高效学习触觉辅助抓取和接触丰富的操作技能。此外，我们介绍了一种方法，通过高效的基于像素的神经模块推断我们的触觉传感器对接触的光学响应。我们预期DIFFTACTILE将作为一个有用的平台，用于研究接触丰富的操作，利用密集触觉反馈和可微分物理的优势。代码和补充材料可在项目网站https://difftactile.github.io/获取。

英文摘要：

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/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。