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arXiv · 2303.02293

Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise

Rui Liu·Guangyao Shi·Pratap Tokekar
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Abstract

Distributionally Robust Optimal Control (DROC) is a framework that enables robust control in a stochastic setting where the true disturbance distribution is unknown. Traditional DROC approaches require given ambiguity sets and KL divergence bounds to represent the distributional uncertainty; however, these quantities are often unavailable a priori or require manual specification. To overcome this limitation, we propose a data-driven approach that jointly estimates the uncertainty distribution and the corresponding KL divergence bound, which we refer to as $\mathrm{D}^3\mathrm{ROC}$. To evaluate the effectiveness of our approach, we consider a car-like robot navigation task with unknown noise distributions. The experimental results show that $\mathrm{D}^3\mathrm{ROC}$ yields robust and effective control policies, outperforming iterative Linear Quadratic Gaussian (iLQG) control and demonstrating strong adaptability to varying noise distributions.

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Rui Liu, Guangyao Shi, Pratap Tokekar. 2023-03-04. Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise. https://arxiv.org/abs/2303.02293

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