We propose a data-driven control method for systems with aleatoric uncertainty, for example, robot fleets with variations between agents. Our method leverages shared trajectory data to increase the robustness of the designed controller and thus facilitate transfer to new variations without the need for prior parameter and uncertainty estimations. In contrast to existing work on experience transfer for performance, our approach focuses on robustness and uses data collected from multiple realizations to guarantee generalization to unseen ones. Our method is based on scenario optimization combined with recent formulations for direct data-driven control. We derive lower bounds on the amount of data required to achieve quadratic stability for probabilistic systems with aleatoric uncertainty and demonstrate the benefits of our data-driven method through a numerical example. We find that the learned controllers generalize well to high variations in the dynamics even when based on only a few short open-loop trajectories. Robust experience transfer enables the design of safe and robust controllers that work out of the box without any additional learning during deployment.

翻译：我们提出了一种针对存在偶然不确定性系统（例如，机器人集群中存在个体差异）的数据驱动控制方法。该方法利用共享轨迹数据来增强所设计控制器的鲁棒性，从而无需事先进行参数和不确定性估计即可实现向新变体的迁移。与现有侧重于性能的经验迁移研究不同，我们的方法聚焦鲁棒性，并利用从多个真实系统中采集的数据来保证对未见系统的泛化能力。本方法基于场景优化与近年来直接数据驱动控制的相关公式推导相结合。我们推导出了在偶然不确定性下实现概率系统二次稳定性所需数据量的下限，并通过数值算例展示了所提数据驱动方法的优势。研究发现，即使仅基于少量短时开环轨迹，学习的控制器也能很好地泛化到高动态变化中。鲁棒经验迁移使得设计开箱即用且无需在部署期间进行任何额外学习的鲁棒安全控制器成为可能。