Behavior-Tree Embeddings for Robot Task-Level Knowledge

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 pre-trained 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.