Towards Adaptive Behavior Trees for Robot Task Planning

Abstract

Behavior Tree, known as modular and reactive, is widely used in industry and research. Its variant, the Reactive Behavior Tree (RBT), can effectively resolve the unpredictable environments through dynamic expansion based on actions and predicates. However, in partially observable environments, considering the observe limitation of scene, the existing RBT expanding method will break the necessary sequential dependence between the predicates and cause the agent to fail to perceive state changes in the environment, leaving the planner in a spin state. We proposed an optimized RBT method, where the RBT can repair the sequential dependence by rollbacking dynamically. We verified the optimized method in a simulation environment, and our method was shown to improve the effectiveness and robustness of RBT in partially observable environments.