Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features
This work addresses the challenge of bridging high-level decision making and control layers in robotics, offering a domain-specific solution that is incremental in nature.
The paper tackled the problem of integrating control features into Behavior Trees for robotics by proposing Reconfigurable Behavior Trees (RBTs), which incorporate physical constraints and continuous sensory information, resulting in a framework that dynamically handles changes in execution context with low execution time, as shown in robotic experiments.
Behavior Trees constitute a widespread AI tool which has been successfully spun out in robotics. Their advantages include simplicity, modularity, and reusability of code. However, Behavior Trees remain a high-level decision making engine; control features cannot be easily integrated. This paper proposes the Reconfigurable Behavior Trees (RBTs), an extension of the traditional BTs that considers physical constraints from the robotic environment in the decision making process. We endow RBTs with continuous sensory information that permits the online monitoring of the task execution. The resulting stimulus-driven architecture is capable of dynamically handling changes in the executive context while keeping the execution time low. The proposed framework is evaluated on a set of robotic experiments. The results show that RBTs are a promising approach for robotic task representation, monitoring, and execution.