论文标题
通过CVAR屏障功能进行风险规划：应用于双皮亚机器人运动
Risk-Averse Planning via CVaR Barrier Functions: Application to Bipedal Robot Locomotion
论文作者
论文摘要
在存在随机不确定性的情况下执行安全性是一个具有挑战性的问题。传统上，研究人员在这种情况下提出了统计平均值作为安全措施的安全性。但是，只有在系统在大量运行中的安全行为引起人们的安全行为时，确保统计平均值的安全性才是合理的，这不仅是在实际情况下无法使用平均安全性。在本文中，我们提出了一种对安全性的风险敏感概念，称为有条件 - 价值 - 风险（CVAR）安全性，这与最坏情况下实现的安全性能有关。我们引入了CVAR屏障功能，作为执行CVAR安全的工具，并为其布尔组成提出条件。鉴于旧版控制器，我们表明我们可以通过求解差异凸面程序来设计最小干扰CVAR安全控制器。我们通过将其应用于两足机器人运动案例研究来阐明提出的方法。
Enforcing safety in the presence of stochastic uncertainty is a challenging problem. Traditionally, researchers have proposed safety in the statistical mean as a safety measure in this case. However, ensuring safety in the statistical mean is only reasonable if system's safe behavior in the large number of runs is of interest, which precludes the use of mean safety in practical scenarios. In this paper, we propose a risk sensitive notion of safety called conditional-value-at-risk (CVaR) safety, which is concerned with safe performance in the worst case realizations. We introduce CVaR barrier functions as a tool to enforce CVaR-safety and propose conditions for their Boolean compositions. Given a legacy controller, we show that we can design a minimally interfering CVaR-safe controller via solving difference convex programs. We elucidate the proposed method by applying it to a bipedal robot locomotion case study.
