Behavior-Tree Embeddings for Robot Task-Level Knowledge
Behavior-Tree Embeddings for Robot Task-Level Knowledge
复制标题
DOI：
10.1109/iros47612.2022.9981774
复制
发表时间：
2022-10
期刊：
影响因子：
--
通讯作者：
Yue Cao;C.S. George Lee
中科院分区：
文献类型：
--
作者：
Yue Cao;C.S. George Lee
最近，行为树作为机器人任务级知识表示越来越受欢迎。从头开始手工设计行为树是乏味和麻烦的。由于需要一种有效的方法来重用或转移机器人任务级知识，我们提出了一种向量空间嵌入方法，将符号任务编码为数值形式。这种方法被称为行为树嵌入，它采用产生单个任务的行为树作为输入，并生成相应的向量。利用预训练的语言嵌入模型和节点聚集机制，生成的嵌入既能保留任务描述的语义信息，又能保留分层任务组织的结构信息。我们在三个不同的任务中评估了我们提出的向量空间嵌入方法的有效性和通用性。
Recently, the behavior tree is gaining popularity as a robotic task-level knowledge representation. Manual design of behavior trees from scratch is tedious and cumbersome. Motivated by the need for an efficient way to reuse or transfer robot task-level knowledge, we propose a vector-space embedding approach that encodes a symbolic task into a numerical form. This approach, called behavior-tree embedding, takes a behavior tree that produces a single task as input and generates a corresponding vector. By exploiting the pretrained language-embedding model and the node-aggregation mechanism, the produced embedding is capable of preserving both semantic information of task description and structural information of the hierarchical task organization. We evaluated the effectiveness and versatility of our proposed vector-space embedding approach in three different tasks.