Behavior Planning

Behavior planning in robotics and autonomous systems focuses on enabling agents to make intelligent decisions about their actions, optimizing for goals like safety, efficiency, and human-like behavior. Current research heavily utilizes large language models (LLMs) and reinforcement learning (RL) to generate diverse and adaptable plans, often integrated with rule-based systems or hierarchical architectures like behavior trees and Monte Carlo Tree Search (MCTS). These advancements are crucial for improving the autonomy and reliability of robots in various applications, from autonomous driving and humanoid manipulation to human-robot interaction and multi-agent coordination in complex environments. The integration of human factors and interpretability into these models is also a growing area of focus.