A Survey of Behavior Tree-Based Task Planning Algorithms for Autonomous Robotic Systems - Shin, Mingyu; Jung, Soyi (International Conference on ICT Convergence, 2024)
Behavior trees (BTs) have gained recognition for their modularity and scalability, establishing them as a robust framework for task automation and planning in various domains, including robotics, game AI, and autonomous systems. This paper presents a comprehensive review of the integration of BTs with reinforcement learning (RL) and learning-from-demonstration (LfD) to enhance decision making and task planning in robotic systems. The review emphasizes the advantages of BTs, such as increased adaptability and efficiency through RL integration and the simplification of robot programming via LfD. Despite these benefits, challenges persist in the areas of computational complexity, scalability in multi-agent systems, and the automatic generation of BTs. This paper concludes by identifying key areas for future research to address these challenges and further advance the development of autonomous robotic systems.
Keywords: Autonomous robotic systems; Autonomous task; Behaviour Trees; Game AI; Learning from demonstration; Planning algorithms; Reinforcement learnings; Task automation; Task planning; Tree-based; Autonomous Robot; Autonomous Task; Behavior Trees; Task Planning