CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation

Behavior trees (BTs) are becoming a popular control architecture for robots, featuring modularity, reactivity, and robustness. BT planning, an emerging approach, provides a theoretical guarantee to generate reliable BTs for achieving tasks automatically. However, BT planning assumes that a well-designed BT system has already been grounded, including high-level action models and low-level control policies, which often requires expensive expert knowledge and effort in the specific domain. In this paper, we define the BT grounding problem, where an algorithm needs to automatically construct a complete and consistent BT system for a given task set. We demonstrate a naive algorithm which is sound and complete for solving this problem, but difficult to be implemented due to the exponential complexity. Then, we propose the first framework for efficiently solving the BT grounding problem, named Context-Aware Behavior Tree grOunding (CABTO). CABTO mainly utilizes pre-trained Large Models (LMs) to heuristically search the space of action models and control policies based on the contexts of BT planning and environmental feedback. Experiments on 3 robot manipulation task sets, involving a total of 15 tasks across different scenarios and robots, demonstrate CABTO’s effectiveness and efficiency in generating complete and consistent behavior tree systems.