Hierarchical Planning

Hierarchical planning aims to solve complex tasks by breaking them down into simpler sub-tasks, enabling efficient and robust problem-solving in robotics and AI. Current research emphasizes integrating various model architectures, including diffusion models, recurrent neural networks, and large language models, often within hierarchical frameworks that combine high-level planning with low-level control. This approach addresses challenges in real-time performance, generalization to unseen environments, and human-robot interaction, with applications ranging from autonomous navigation and manipulation to multi-robot coordination and human-assisted tasks.