Distributionally Robust Optimal Control (DROC) is a technique that enables robust control in a stochastic setting when the true distribution is not known. Traditional DROC approaches require given ambiguity sets or a KL divergence bound to represent the distributional uncertainty. These may not be known a priori and may require hand-crafting. In this paper, we lift this assumption by introducing a data-driven technique for estimating the uncertainty and a bound for the KL divergence. We call this technique D3ROC. To evaluate the effectiveness of our approach, we consider a navigation problem for a car-like robot with unknown noise distributions. The results demonstrate that D3ROC provides robust and efficient control policies that outperform the iterative Linear Quadratic Gaussian (iLQG) control. The results also show the effectiveness of our proposed approach in handling different noise distributions.

翻译：分布鲁棒最优控制（DROC）是一种在真实分布未知的随机环境中实现鲁棒控制的技术。传统DROC方法需要预先给定模糊集或KL散度界来描述分布不确定性，这些参数可能无法先验获取，往往需要人为设定。本文通过引入数据驱动的不确定性估计技术及KL散度边界估计，突破了这一假设局限，提出名为D3ROC的方法。为评估该方法的有效性，我们以具有未知噪声分布的类车机器人导航问题为测试场景。结果表明，D3ROC能够生成鲁棒且高效的控制策略，其性能优于迭代线性二次型高斯（iLQG）控制。研究同时验证了该方法在处理不同噪声分布时的有效性。