论文标题
基于基于图的空间时间逻辑的自主机器人的规范挖掘和自动化任务计划
Specification mining and automated task planning for autonomous robots based on a graph-based spatial temporal logic
论文作者
论文摘要
我们的目标是使一个自主机器人从演示视频中学习新技能，并使用这些新知识的技能来完成非平凡的高级任务。开发此类自主机器人的目的涉及知识表示，规格挖掘和自动化任务计划。对于知识表示，我们使用基于图的空间时间逻辑（GSTL）来捕获演示视频所展示的相关技能的空间和时间信息。我们设计了一种规范挖掘算法，以通过电感构造空间术语和时间公式来从演示视频中生成一组参数GSTL公式。所得的参数GSTL公式来自规范挖掘作为域理论，该公式用于自主机器人的自动任务计划。我们提出了一个基于GSTL的自动任务计划，该计划使用建议者来生成有序的操作，并使用验证者来生成可执行的任务计划。在整篇文章中使用表设置示例来说明主要思想。
We aim to enable an autonomous robot to learn new skills from demo videos and use these newly learned skills to accomplish non-trivial high-level tasks. The goal of developing such autonomous robot involves knowledge representation, specification mining, and automated task planning. For knowledge representation, we use a graph-based spatial temporal logic (GSTL) to capture spatial and temporal information of related skills demonstrated by demo videos. We design a specification mining algorithm to generate a set of parametric GSTL formulas from demo videos by inductively constructing spatial terms and temporal formulas. The resulting parametric GSTL formulas from specification mining serve as a domain theory, which is used in automated task planning for autonomous robots. We propose an automatic task planning based on GSTL where a proposer is used to generate ordered actions, and a verifier is used to generate executable task plans. A table setting example is used throughout the paper to illustrate the main ideas.
