Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking
Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking
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DOI：
10.1109/lra.2022.3191068
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发表时间：
2022-07
影响因子：
5.2
通讯作者：
Md. Sadman Sakib;D. Paulius;Yu Sun
中科院分区：
文献类型：
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作者：
Md. Sadman Sakib;D. Paulius;Yu Sun
灵活的任务规划继续给机器人带来困难，机器人无法创造性地调整任务计划来应对新的或未知的问题，这主要是因为它对自己的行动和世界的了解有限。受人类适应能力的激励，我们探索如何从知识图中生成任务计划，称为功能面向对象网络(FOON)，以解决需要概念的新问题，而这些概念在机器人的知识库中并不容易获得。来自140个烹饪食谱的知识被组织在FOON知识图中，该图用于获取称为任务树的任务计划序列。任务树可以被修改为以FOON知识图格式复制食谱，这对于通过依赖语义相似性用包含未知对象和状态组合的新食谱来丰富FOON是有用的。我们展示了任务树生成的强大功能，可以创建具有前所未有的成分和状态组合的任务树，如Recipe1M+数据集中的食谱所示，我们根据树描述新添加的成分的准确性来评估树的质量。实验结果表明，该系统能够以76%的正确率提供任务序列。
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.