论文标题
用于机器人烹饪的知识网络中的近似任务树检索
Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking
论文作者
论文摘要
灵活的任务计划继续对机器人构成艰巨的挑战，在该机器人中，机器人无法创造性地将其任务计划适应新的或看不见的问题，这主要是由于它对其行动和世界的知识有限。通过人类适应能力的动机，我们探索了如何从知识图（称为功能对象的网络（FOON））中探索的任务计划，该计划是针对需要在其知识库中不容易获得概念的新颖问题而生成的。来自140种烹饪食谱的知识是在FOON知识图中构造的，该图用于获取称为任务树的任务计划序列。可以修改任务树以以Foon知识图格式复制食谱，这对于通过依靠语义相似性来丰富Foon使用包含未知对象和状态组合的新食谱很有用。我们演示了任务树生成的力量，可以在食谱1M+数据集中看到的食谱中看到，以前看不见的成分和状态组合创建任务树的功能，我们根据它们的精确描述了新添加的成分的方式来评估树的质量。我们的实验结果表明，我们的系统能够提供76％正确性的任务序列。
Flexible task planning continues to pose a difficult challenge for robots, where a robot is unable to creatively adapt their task plans to new or unseen problems, which is mainly due to the limited knowledge it has about its actions and world. Motivated by a human's ability to adapt, we explore how task plans from a knowledge graph, known as the Functional Object- Oriented Network (FOON), can be generated for novel problems requiring concepts that are not readily available to the robot in its knowledge base. Knowledge from 140 cooking recipes are structured in a FOON knowledge graph, which is used for acquiring task plan sequences known as task trees. Task trees can be modified to replicate recipes in a FOON knowledge graph format, which can be useful for enriching FOON with new recipes containing unknown object and state combinations, by relying upon semantic similarity. We demonstrate the power of task tree generation to create task trees with never-before-seen ingredient and state combinations as seen in recipes from the Recipe1M+ dataset, with which we evaluate the quality of the trees based on how accurately they depict newly added ingredients. Our experimental results show that our system is able to provide task sequences with 76% correctness.
