カーネル分布埋め込みを用いたデータ駆動型チャンス制約制御
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
カーネル分布埋め込みを利用し、システムの事前知識なしに観測軌道データから確率的制約付き最適制御問題を線形計画として効率的に解く手法を提案した。
著者: Adam J. Thorpe, Thomas Lew, Meeko M. K. Oishi, Marco Pavone
分類: eess.SY, cs.RO, cs.SY, math.OC
原文アブストラクト
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems. Our approach leverages the theory of kernel distribution embeddings, which allows representing expectation operators as inner products in a reproducing kernel Hilbert space. This framework enables approximately reformulating the original problem using a dataset of observed trajectories from the system without imposing prior assumptions on the parameterization of the system dynamics or the structure of the uncertainty. By optimizing over a finite subset of stochastic open-loop control trajectories, we relax the original problem to a linear program over the control parameters that can be efficiently solved using standard convex optimization techniques. We demonstrate our proposed approach in simulation on a system with nonlinear non-Markovian dynamics navigating in a cluttered environment.
