论文标题
物理上可行的反应性，线性时间逻辑的高级任务
Physically-Feasible Repair of Reactive, Linear Temporal Logic-based, High-Level Tasks
论文作者
论文摘要
创建复杂机器人行为的一种典型方法是组成原子控制器或技能，以使所得的行为满足高级任务；但是，当无法通过一组给定的技能完成任务时，很难知道如何修改技能以使任务成为可能。我们提出了一种将符号维修与身体可行性检查和实现相结合的方法，以自动修改现有技能，以便机器人可以执行以前不可行的任务。 我们在线性时间逻辑（LTL）公式中编码机器人技能，以捕获安全性任务的安全限制和目标。此外，我们的编码捕获了完整的技能执行，而不是先前的工作，而在执行技能之前和之后只有世界状态才被考虑。我们的维修算法提出了符号修改，然后尝试通过修改根据符号维修得出的LTL约束的原始技能来物理实施建议。如果技能不可能，我们将自动为符号维修提供其他约束。我们用巴克斯特（Baxter）和清晰的道路jack来演示我们的方法。
A typical approach to creating complex robot behaviors is to compose atomic controllers, or skills, such that the resulting behavior satisfies a high-level task; however, when a task cannot be accomplished with a given set of skills, it is difficult to know how to modify the skills to make the task possible. We present a method for combining symbolic repair with physical feasibility-checking and implementation to automatically modify existing skills such that the robot can execute a previously infeasible task. We encode robot skills in Linear Temporal Logic (LTL) formulas that capture both safety constraints and goals for reactive tasks. Furthermore, our encoding captures the full skill execution, as opposed to prior work where only the state of the world before and after the skill is executed are considered. Our repair algorithm suggests symbolic modifications, then attempts to physically implement the suggestions by modifying the original skills subject to LTL constraints derived from the symbolic repair. If skills are not physically possible, we automatically provide additional constraints for the symbolic repair. We demonstrate our approach with a Baxter and a Clearpath Jackal.
