Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration. While aleatoric uncertainty that arises from measurement noise can often be explicitly modeled given a parametric description, it can be harder to model epistemic uncertainty, which describes the presence or absence of training data. The latter can be particularly useful for implementing exploratory control strategies when system dynamics are unknown. We propose a novel method for detecting the absence of training data using deep learning, which gives a continuous valued scalar output between $0$ (indicating low uncertainty) and $1$ (indicating high uncertainty). We utilize this detector as a proxy for epistemic uncertainty and show its advantages over existing approaches on synthetic and real-world datasets. Our approach can be directly combined with aleatoric uncertainty estimates and allows for uncertainty estimation in real-time as the inference is sample-free unlike existing approaches for uncertainty modeling. We further demonstrate the practicality of this uncertainty estimate in deploying online data-efficient control on a simulated quadcopter acted upon by an unknown disturbance model.

翻译：在未知环境中进行数据驱动控制需要清晰理解所涉及的不确定性，以确保安全性和高效探索。尽管由测量噪声引起的偶然不确定性通常可以在给定参数描述时显式建模，但描述训练数据存在与否的认知不确定性则更难建模。当系统动力学未知时，后者对于实施探索性控制策略尤为有用。我们提出了一种利用深度学习检测训练数据缺失的新方法，该方法生成介于0（表示低不确定性）和1（表示高不确定性）之间的连续值标量输出。我们将该检测器用作认知不确定性的代理，并在合成数据集和真实数据集上展示了其相较于现有方法的优势。我们的方法可直接与偶然不确定性估计相结合，并且由于推理无需采样（不同于现有的不确定性建模方法），因此能够实现实时不确定性估计。我们进一步通过一个受未知扰动模型作用的模拟四旋翼飞行器上的在线数据高效控制实验，证明了这种不确定性估计的实用性。