Robotics traditionally operates within two dominant architectures: modular hierarchical policies and end-to-end policies. The former uses rigid layers like symbolic planning, trajectory generation, and tracking, whereas the latter uses high-capacity neural networks to directly connect sensory input to actions. Large language models (LLMs) have rejuvenated the interest in hierarchical control architectures, with researchers using LLMs…
