论文标题
通过可区分的物理引擎对弹簧杆系统进行数据效率系统识别的第一原理方法
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
论文作者
论文摘要
我们提出了一种新型的可区分物理引擎，用于系统识别复杂的弹簧杆组件。与黑框数据驱动的方法用于学习动态系统及其参数的演变不同，我们使用类似于传统物理发动机的管理方程式的离散形式的运动方程式将发动机的设计模块化。对于每个模块，我们进一步将尺寸从3D降低到1D，从而可以使用线性回归有效学习系统参数。作为副益处，回归参数对应于物理量，例如弹簧刚度或杆的质量，使管道可解释。该方法大大减少了所需的培训数据量，并且还避免了对数据采样和模型培训的迭代识别。我们将提出的引擎的性能与以前的解决方案进行了比较，并在诸如NASA的Icosahedron等张力系统上证明了其功效。
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dynamical system and its parameters, we modularize the design of our engine using a discrete form of the governing equations of motion, similar to a traditional physics engine. We further reduce the dimension from 3D to 1D for each module, which allows efficient learning of system parameters using linear regression. As a side benefit, the regression parameters correspond to physical quantities, such as spring stiffness or the mass of the rod, making the pipeline explainable. The approach significantly reduces the amount of training data required, and also avoids iterative identification of data sampling and model training. We compare the performance of the proposed engine with previous solutions, and demonstrate its efficacy on tensegrity systems, such as NASA's icosahedron.
