This paper presents a framework that enables robots to automatically recover from assumption violations of high-level specifications during task execution. In contrast to previous methods relying on user intervention to impose additional assumptions for failure recovery, our approach leverages synthesis-based repair to suggest new robot skills that, when implemented, repair the task. Our approach detects violations of environment safety assumptions during the task execution, relaxes the assumptions to admit observed environment behaviors, and acquires new robot skills for task completion. We demonstrate our approach with a Hello Robot Stretch in a factory-like scenario.

翻译：本文提出一个框架，使机器人能够在任务执行期间自动从高层规范的假设违反中恢复。与以往依赖用户干预以施加额外假设进行故障恢复的方法不同，我们的方法利用基于综合的修复来建议新的机器人技能，这些技能一旦实施，即可修复任务。我们的方法在任务执行期间检测环境安全假设的违反，放宽假设以接纳观察到的环境行为，并获取新的机器人技能以完成任务。我们在一个类工厂场景中使用Hello Robot Stretch演示了我们的方法。