  In the pursuit of fully autonomous robotic systems capable of taking over
tasks traditionally performed by humans, the complexity of open-world
environments poses a considerable challenge. Addressing this imperative, this
study contributes to the field of Large Language Models (LLMs) applied to task
and motion planning for robots. We propose a system architecture that
orchestrates a seamless interplay between multiple cognitive levels,
encompassing reasoning, planning, and motion generation. At its core lies a
novel replanning strategy that handles physically grounded, logical, and
semantic errors in the generated plans. We demonstrate the efficacy of the
proposed feedback architecture, particularly its impact on executability,
correctness, and time complexity via empirical evaluation in the context of a
simulation and two intricate real-world scenarios: blocks world, barman and
pizza preparation.
