Soft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging due to the complex physics of deformable materials. Accurate Finite Element Method (FEM) models incur prohibitive computational complexity for closed-loop use. Using a differentiable simulator is an attractive alternative, but their applicability to soft actuators and deformable materials remains underexplored. This paper presents a framework that combines the advantages of both. We learn a differentiable model consisting of a material properties neural network and an analytical dynamics model of the remainder of the manipulation task. This physics-informed model is trained using data generated from FEM, and can be used for closed-loop control and inference. We evaluate our framework on a dielectric elastomer actuator (DEA) coin-pulling task. We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference. Our model attains < 5% simulation error compared to FEM, and we use it as the basis for an MPC controller that requires fewer iterations to converge than model-free actor-critic, PD, and heuristic policies.
Portable, High-Frequency, and High-Voltage Control Circuits for Untethered Miniature Robots Driven by Dielectric Elastomer Actuators
Department of Mechanical Engineering, State Key Laboratory of Tribology in Advanced Equipment, Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology, Tsinghua University（机械工程系、先进设备摩擦学国家重点实验室、北京 transformative 高端制造装备与技术重点实验室、清华大学）
Simplified discrete model for axisymmetric dielectric elastomer membranes with robotic applications
Hohai University（河海大学）；University of Birmingham（伯明翰大学）；Nanyang Technological University（南洋理工大学）；University of Michigan（密歇根大学）；Newcastle University（纽卡斯尔大学）
University of Connecticut, School of Mechanical, Aerospace, and Manufacturing Engineering（康奈尔大学机械、航空航天与制造工程学院）；Institute of Macromolecular Chemistry Petru Poni（彼得·波尼宏观分子化学研究所）；University of Toronto, Department of Mechanical and Industrial Engineering（多伦多大学机械与工业工程系）
Force Generation by Parallel Combinations of Fiber-Reinforced Fluid-Driven Actuators
University of Michigan（密歇根大学）
与传统的刚体机器人相比，柔性机器人系统的柔性结构能够实现各种新颖的功能。被称为纤维增强弹性体外壳（FREE）的软流体驱动致动器的子类特别适合作为这些类型的系统的致动器。自由弹性体本质上是柔软的，可以在不施加刚性结构的情况下施加空间力。此外，它们可以被配置为产生各种各样的力和力矩组合。在本文中，我们探讨的潜力，结合多个不同配置的自由电子元件并联，以实现完全可控的多维软驱动。为此，我们提出了一种新的方法来表示和计算的广义软致动器所产生的力作为其内部压力的函数。这种方法依赖于状态相关的流体雅可比矩阵的概念，产生力的线性表达式。我们利用这个概念来构造一个软系统在给定状态下可能产生的所有可能的力的集合。这种力zonotope可以用来通知软致动器的并联组合的设计和控制。实验验证了该方法的并行组合的三个精心设计的致动器约束到一个2DOF的测试台。力的预测值与测量值相匹配，均方根误差小于1.5 N力和8 x 10^（-3）Nm力矩，证明了所提出的方法的实用性。
A Robotic Testing Platform for Pipelined Discovery of Resilient Soft Actuators
Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Institute of Materials Science, University of Connecticut（康涅狄格大学材料科学研究所）；School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut（康涅狄格大学机械、航空航天与制造工程学院）；Material Science and Engineering, University of Connecticut（康涅狄格大学材料科学与工程系）；Inorganic Polymers Department, Petru Poni Institute of Macromolecular Chemistry（彼得·波尼宏分子化学研究所无机聚合物部门）
Programmable Deformation Design of Porous Soft Actuator through Volumetric-Pattern-Induced Anisotropy
ShanghaiTech Automation and Robotics (STAR) Center, School of Information Science and Technology, ShanghaiTech University（上海科技大学自动化与机器人（STAR）中心，信息科学与技术学院，上海科技大学）
Power from Potential: A Survey of Electrostatic Actuators for Haptics
Stanford University（斯坦福大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Artimus Robotics（阿蒂姆斯机器人公司）；Chinese Academy of Science（中国科学院）；Fluid Reality, Inc.（流体现实公司）；Cornell University（康奈尔大学）
Miniaturized Circuitry for Capacitive Self-sensing and Closed-loop Control of Soft Electrostatic Transducers
University of Colorado（科罗拉多大学）；Materials Science and Engineering Program, University of Colorado（科罗拉多大学材料科学与工程项目）；Department of Electrical Engineering, University of Colorado（科罗拉多大学电气工程系）；Department of Computer Science, University of Colorado（科罗拉多大学计算机科学系）
