Simulation is an important step in robotics for creating control policies and testing various physical parameters. Soft robotics is a field that presents unique physical challenges for simulating its subjects due to the nonlinearity of deformable material components along with other innovative, and often complex, physical properties. Because of the computational cost of simulating soft and heterogeneous objects with traditional techniques, rigid robotics simulators are not well suited to simulating soft robots. Thus, many engineers must build their own one-off simulators tailored to their system, or use existing simulators with reduced performance. In order to facilitate the development of this exciting technology, this work presents an interactive-speed, accurate, and versatile simulator for a variety of types of soft robots. Cronos, our open-source 3D simulation engine, parallelizes a mass-spring model for ultra-fast performance on both deformable and rigid objects. Our approach is applicable to a wide array of nonlinear material configurations, including high deformability, volumetric actuation, or heterogenous stiffness. This versatility provides the ability to mix materials and geometric components freely within a single robot simulation. By exploiting the flexibility and scalability of nonlinear Hookean mass-spring systems, this framework simulates soft and rigid objects via a highly parallel model for near real-time speed. We describe an efficient GPU CUDA implementation, which we demonstrate to achieve computation of over 1 billion elements per second on consumer-grade GPU cards. Dynamic physical accuracy of the system is validated by comparing results to Euler-Bernoulli beam theory, natural frequency predictions, and empirical data of a soft structure under large deformation.
Developing Simulation Models for Soft Robotic Grippers in Webots
Faculty of Science and Engineering, University of Groningen（格罗宁根大学科学与工程学院）；School of Electrical Engineering and Informatics, Bandung Institute of Technology（班达理工大学电气工程与信息学院）
Real-World Deployment of Massively Parallel Sampling-Based MPC for Contact-Rich Manipulation
Intelligent Autonomous Systems Lab, TU Darmstadt（达姆施塔特工业大学智能自主系统实验室）；Interactive Robot Perception & Learning Lab, TU Darmstadt（达姆施塔特工业大学交互式机器人感知与学习实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Robotics Institute Germany (RIG)（德国机器人研究所）；College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
NVIDIA（英伟达）；University of Southern California（南加州大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Washington（华盛顿大学）；University of Sydney（悉尼大学）
SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
Fudan University, China（复旦大学）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Shanghai Jiao Tong University, China（上海交通大学）；The Chinese University of Hong Kong, China（香港中文大学）；The University of Hong Kong, China（香港大学）
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
University of Southern California（南加州大学）；NVIDIA（英伟达）；University of Washington（华盛顿大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Sydney（悉尼大学）
RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning
Department of Computer Science, Duke University（达特茅斯大学计算机科学系）；Department of Mechanical Engineering and Material Science（机械工程与材料科学系）；Department of Electrical and Computer Engineering（电气与计算机工程系）
