離散オイラー・ラグランジュ残差最小化による構造化力学モデルの学習
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
構造化力学モデルを離散オイラー・ラグランジュ残差の最小化で学習する手法を提案し、二重振り子の時系列データで従来法より高精度なモデルが得られることを示した。
著者: Kunal Menda, Jayesh K. Gupta, Zachary Manchester, Mykel J. Kochenderfer
分類: cs.RO, cs.LG
原文アブストラクト
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.
