Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual

TL;DR

This paper introduces a new method for fitting Structured Mechanical Models to data by minimizing the discrete Euler-Lagrange residual, improving accuracy over traditional fitting schemes, especially in noisy conditions.

Contribution

The paper presents a novel approach for learning SMMs through discrete Euler-Lagrange residual minimization, enhancing model accuracy and robustness.

Findings

Better accuracy than conventional fitting methods.

Effective in noisy data scenarios.

Applicable to various mechanical systems.

Abstract

Model-based paradigms for decision-making and control are becoming ubiquitous in robotics. They rely on the ability to efficiently learn a model of the system from data. Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states. In this work, we propose a methodology for fitting SMMs to data by minimizing the discrete Euler-Lagrange residual. To study our methodology, we fit models to joint-angle time-series from undamped and damped double-pendulums, studying the quality of learned models fit to data with and without observation noise. Experiments show that our methodology learns models that are better in accuracy to those of the conventional schemes for fitting SMMs. We identify use cases in which our method is a more appropriate methodology. Source code for reproducing the experiments is available at https://github.com/sisl/delsmm.