论文标题
具有功能性对象的网络的长马计划和执行
Long-Horizon Planning and Execution with Functional Object-Oriented Networks
论文作者
论文摘要
在对关节对象表示表示的工作之后，引入了面向对象的网络（FOON）作为机器人的知识图表示。 FOON包含符号概念，可用于机器人对任务及其对象级别计划的环境的理解。在这项工作之前，几乎没有什么可以展示从Foon获取的计划如何由机器人执行的，因为Foon中的概念太抽象了，无法执行。因此，我们介绍了利用对象级知识作为任务计划和执行的FOON的想法。我们的方法会自动将FOON转换为PDDL，并在层次规划管道中利用现成的计划者，行动环境和机器人技能，以生成可执行的任务计划。我们演示了我们在Coppeliasim中长跑任务的整个方法，并展示了如何将学习的动作环境扩展到从未见过的场景。
Following work on joint object-action representations, functional object-oriented networks (FOON) were introduced as a knowledge graph representation for robots. A FOON contains symbolic concepts useful to a robot's understanding of tasks and its environment for object-level planning. Prior to this work, little has been done to show how plans acquired from FOON can be executed by a robot, as the concepts in a FOON are too abstract for execution. We thereby introduce the idea of exploiting object-level knowledge as a FOON for task planning and execution. Our approach automatically transforms FOON into PDDL and leverages off-the-shelf planners, action contexts, and robot skills in a hierarchical planning pipeline to generate executable task plans. We demonstrate our entire approach on long-horizon tasks in CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.
