论文标题
通过学习抽象表示的自动编码和修复反应性高级任务
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations
论文作者
论文摘要
我们提出了一个框架，鉴于机器人可以执行的一组技能，将传感器数据抽象成我们用来自动用线性时间逻辑编码机器人功能的符号。我们根据这些功能指定反应性的高级任务，如果任务可行，则在机器人上自动合成和执行策略。如果鉴于机器人的功能，任务不可行，我们会提出两种方法，一种基于枚举的方法，一种基于综合的方法，用于自动为机器人提出其他技能，或对现有技能进行修改，以使任务可行。我们在桌子上操纵块的百特机器人上演示了我们的框架，桌子上的baxter机器人操纵板，而kinova手臂操纵小瓶，具有多种传感器方式，包括原始图像。
We present a framework that, given a set of skills a robot can perform, abstracts sensor data into symbols that we use to automatically encode the robot's capabilities in Linear Temporal Logic. We specify reactive high-level tasks based on these capabilities, for which a strategy is automatically synthesized and executed on the robot, if the task is feasible. If a task is not feasible given the robot's capabilities, we present two methods, one enumeration-based and one synthesis-based, for automatically suggesting additional skills for the robot or modifications to existing skills that would make the task feasible. We demonstrate our framework on a Baxter robot manipulating blocks on a table, a Baxter robot manipulating plates on a table, and a Kinova arm manipulating vials, with multiple sensor modalities, including raw images.
