RAT iLQR：確率的モデル誤差に最適に対処するリスク自動調整コントローラ
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
ガウス分布からのKLダイバージェンス制約下で最悪ケース分布に対する分布ロバスト制御を実現する非線形MPCを提案し、リスク感度をオンラインで自動調整するアルゴリズムを開発した。
著者: Haruki Nishimura, Negar Mehr, Adrien Gaidon, Mac Schwager
分類: cs.RO
原文アブストラクト
Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited knowledge. This work presents a control algorithm that is capable of handling such distributional mismatches. Specifically, we propose a novel nonlinear MPC for distributionally robust control, which plans locally optimal feedback policies against a worst-case distribution within a given KL divergence bound from a Gaussian distribution. Leveraging mathematical equivalence between distributionally robust control and risk-sensitive optimal control, our framework also provides an algorithm to dynamically adjust the risk-sensitivity level online for risk-sensitive control. The benefits of the distributional robustness as well as the automatic risk-sensitivity adjustment are demonstrated in a dynamic collision avoidance scenario where the predictive distribution of human motion is erroneous.
