论文标题
统计安全性和鲁棒性保证反馈运动计划未知未知的随机系统
Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems
论文作者
论文摘要
我们提出了一种为运行时安全性和目标达到的统计保证提供的方法，以综合计划和控制一类非线性随机动态的系统。具体而言，考虑到动力学数据集，我们的方法共同学习一个平均动力学模型，一种捕获噪声和模型不匹配的效果的空间变化的干扰结合，以及基于收缩理论的反馈控制器，稳定了学习的动力学。我们提出了一个基于抽样的计划者，该计划者使用平均动力学模型，并通过学习的干扰结合同时界定闭环跟踪误差。我们采用从极值理论（EVT）到估算到指定的信心水平的技术，这些常数是特征有学识渊博的组件并控制跟踪误差绑定的大小的常数。这样可以确保确保计划在运行时安全地跟踪。我们证实了我们的保证在10D四极管上模拟中的经验安全性，以及在现实世界中使用物理疯狂的四肢旋转器和ClearPath Jackal机器人，而忽略模型误差和随机性的基线是不安全的。
We present a method for providing statistical guarantees on runtime safety and goal reachability for integrated planning and control of a class of systems with unknown nonlinear stochastic underactuated dynamics. Specifically, given a dynamics dataset, our method jointly learns a mean dynamics model, a spatially-varying disturbance bound that captures the effect of noise and model mismatch, and a feedback controller based on contraction theory that stabilizes the learned dynamics. We propose a sampling-based planner that uses the mean dynamics model and simultaneously bounds the closed-loop tracking error via a learned disturbance bound. We employ techniques from Extreme Value Theory (EVT) to estimate, to a specified level of confidence, several constants which characterize the learned components and govern the size of the tracking error bound. This ensures plans are guaranteed to be safely tracked at runtime. We validate that our guarantees translate to empirical safety in simulation on a 10D quadrotor, and in the real world on a physical CrazyFlie quadrotor and Clearpath Jackal robot, whereas baselines that ignore the model error and stochasticity are unsafe.
