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

arXiv: https://arxiv.org/abs/2303.02293 · HTML: https://mlsanity.com/paper/2303.02293/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2023.

## Built on by (most cited first, 5 of 5)

- [Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis](https://mlsanity.com/paper/2403.08955/) (2024)
- [CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems](https://mlsanity.com/paper/2502.17821/) (2025)
- [Adaptive Conformal Guidance for Learning under Uncertainty](https://mlsanity.com/paper/2502.16736/) (2025)
- [MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation](https://mlsanity.com/paper/2509.18198/) (2025)
- [Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty](https://mlsanity.com/paper/2602.04763/) (2026)

## Abstract (arXiv)

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.