A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines

TL;DR

This paper introduces a physics-based differentiable engine for efficient system identification of spring-rod assemblies, reducing data needs and enhancing interpretability compared to black-box methods.

Contribution

The paper presents a modular, physics-inspired differentiable engine that simplifies system identification by reducing dimensions and enabling linear regression for physical parameters.

Findings

Reduces training data requirements significantly.

Provides physically interpretable parameters like spring stiffness.

Demonstrates effectiveness on NASA's tensegrity systems.

Abstract

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.