High Level Task

High-level task research focuses on enabling agents, robots, and systems to perform complex, multi-step operations, often by decomposing them into simpler sub-tasks. Current efforts concentrate on improving efficiency through hierarchical reinforcement learning and bilevel optimization, leveraging techniques like Behavior Trees and employing models such as autoencoders and LLMs to manage task complexity and data scale. These advancements are crucial for creating more robust and adaptable autonomous systems across diverse applications, including robotics, human-computer interaction, and resource allocation problems.