物体数可変環境のための合成Koopmanオペレータ学習
Learning Compositional Koopman Operators for Model-Based Control
グラフニューラルネットで物体中心の埋め込みを構成し、ブロック単位の線形遷移行列でKoopmanオペレータを学習することで、物体数が変化する環境でもモデルベース制御を可能にした。
著者: Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba
分類: cs.LG, cs.RO, math.OC, stat.ML
原文アブストラクト
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods. Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators. These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects. In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects. The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal. Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines.
