データ駆動型スペクトル部分多様体縮約による高次元ロボットの非線形最適制御
Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots
データから低次元のアトラクタ上の力学を抽出するスペクトル部分多様体縮約（SSMR）を提案し、高次元ロボットの非線形最適制御において、訓練データと大きく異なる軌道追従でも既存手法より優れることを示した。
著者: John Irvin Alora, Mattia Cenedese, Edward Schmerling, George Haller, Marco Pavone
分類: cs.RO
原文アブストラクト
Modeling and control of high-dimensional, nonlinear robotic systems remains a challenging task. While various model- and learning-based approaches have been proposed to address these challenges, they broadly lack generalizability to different control tasks and rarely preserve the structure of the dynamics. In this work, we propose a new, data-driven approach for extracting low-dimensional models from data using Spectral Submanifold Reduction (SSMR). In contrast to other data-driven methods which fit dynamical models to training trajectories, we identify the dynamics on generic, low-dimensional attractors embedded in the full phase space of the robotic system. This allows us to obtain computationally-tractable models for control which preserve the system's dominant dynamics and better track trajectories radically different from the training data. We demonstrate the superior performance and generalizability of SSMR in dynamic trajectory tracking tasks vis-a-vis the state of the art, including Koopman operator-based approaches.
