Foundation-Model-Based Action Selection for Behavior Trees in Navigation

Behavior Trees are a promising approach to model the autonomous behaviour of robots in dynamic environments. Behavior Trees represent action selection decisions as a tree of decision nodes. The hierarchy of these decision nodes provides the planning of actions of the robot including its reactions on exceptions. Behavior Trees enable flexible planning and replanning of robot behavior while supporting better maintainable decision-making than traditional Finite State Machines. This paper presents an overview of lessons, which we have learned when applying Behavior Trees to various autonomous robots. We present these lessons as a sequence of steps that is meant to support robot software practitioners to develop their systems.

Evacuation procedure in building fire has several points, and the main destination for occupants are going through the exit door of the building safely. In this research we learn how occupants behave with the emotion influence in a building fire. Emotions have important role to affect decision making because it can increase or decrease the rational value and aim at being realistic and naturally. The possibility to make NPCs behave naturally are implement multi behavior and handle all behavior using artificial intelligence (AI) technique. AI architecture have to support variant behavior and easily to reuse for complex character behaviors. Behavior trees (BTs) is the one of many AI techniques that more readable and scalable for action selection mechanism. So, we propose to implement Emotional Behavior Trees (EmoBTs) to handle dynamic behavior scenario that influenced by emotions. We already made the scenario for NPCs, that have emotion to decide which act would be selected. The experiment result compare how each NPCs are going to act by the emotions influence.