  Robotic planning algorithms direct agents to perform actions within diverse
environments to accomplish a task. Large Language Models (LLMs) like PaLM 2,
GPT-3.5, and GPT-4 have revolutionized this domain, using their embedded
real-world knowledge to tackle complex tasks involving multiple agents and
objects. This paper introduces an innovative planning algorithm that integrates
LLMs into the robotics context, enhancing task-focused execution and success
rates. Key to our algorithm is a closed-loop feedback which provides real-time
environmental states and error messages, crucial for refining plans when
discrepancies arise. The algorithm draws inspiration from the human neural
system, emulating its brain-body architecture by dividing planning across two
LLMs in a structured, hierarchical fashion. Our method not only surpasses
baselines within the VirtualHome Environment, registering a notable 35% average
increase in task-oriented success rates, but achieves an impressive execution
score of 85%, approaching the human-level benchmark of 94%. Moreover,
effectiveness of the algorithm in real robot scenarios is shown using a
realistic physics simulator and the Franka Research 3 Arm.
