We consider perception-based control using state estimates that are obtained from high-dimensional sensor measurements via learning-enabled perception maps. However, these perception maps are not perfect and result in state estimation errors that can lead to unsafe system behavior. Stochastic sensor noise can make matters worse and result in estimation errors that follow unknown distributions. We propose a perception-based control framework that i) quantifies estimation uncertainty of perception maps, and ii) integrates these uncertainty representations into the control design. To do so, we use conformal prediction to compute valid state estimation regions, which are sets that contain the unknown state with high probability. We then devise a sampled-data controller for continuous-time systems based on the notion of measurement robust control barrier functions. Our controller uses idea from self-triggered control and enables us to avoid using stochastic calculus. Our framework is agnostic to the choice of the perception map, independent of the noise distribution, and to the best of our knowledge the first to provide probabilistic safety guarantees in such a setting. We demonstrate the effectiveness of our proposed perception-based controller for a LiDAR-enabled F1/10th car.

翻译：我们考虑使用通过学习型感知映射从高维传感器测量中获取的状态估计进行感知控制。然而，这些感知映射并非完美无缺，会导致状态估计误差，进而引发不安全的系统行为。随机传感器噪声可能使情况恶化，并产生服从未知分布的估计误差。我们提出一种感知控制框架，该框架能够：i) 量化感知映射的估计不确定性，以及 ii) 将这些不确定性表示集成到控制设计中。为此，我们采用共形预测来计算有效的状态估计区域——这些集合能以高概率包含未知状态。随后，我们基于测量鲁棒控制障碍函数的概念，为连续时间系统设计了一种采样数据控制器。该控制器借鉴了自触发控制的思想，使我们能够避免使用随机微积分。我们的框架与感知映射的选择无关，独立于噪声分布，并且据我们所知，是首个在此类场景下提供概率性安全保证的方法。我们通过一辆配备激光雷达的F1/10th比例汽车验证了所提出的感知控制器的有效性。