Reactive Planning

Reactive planning focuses on enabling robots and autonomous systems to make decisions and adapt their actions in real-time based on immediate sensory input, rather than relying on pre-computed plans. Current research emphasizes improving the robustness and efficiency of reactive planners, particularly by integrating machine learning models like large language models and neural networks to handle uncertainty and escape local minima, often within model predictive control frameworks. These advancements are crucial for creating safer and more adaptable robots capable of operating effectively in complex and unpredictable environments, with applications ranging from autonomous driving to collaborative robotics.