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

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

4 Mar 2023arXiv:2303.02293links table onlyarchive 2025-07-28

Rui Liu, Guangyao Shi, Pratap Tokekar

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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 D³ROC. To evaluate the effectiveness of our approach, we consider a car-like robot navigation task with unknown noise distributions. The experimental results show that D³ROC 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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