  Large Language Models (LLMs) possess extensive foundational knowledge and
moderate reasoning abilities, making them suitable for general task planning in
open-world scenarios. However, it is challenging to ground a LLM-generated plan
to be executable for the specified robot with certain restrictions. This paper
introduces CLMASP, an approach that couples LLMs with Answer Set Programming
(ASP) to overcome the limitations, where ASP is a non-monotonic logic
programming formalism renowned for its capacity to represent and reason about a
robot's action knowledge. CLMASP initiates with a LLM generating a basic
skeleton plan, which is subsequently tailored to the specific scenario using a
vector database. This plan is then refined by an ASP program with a robot's
action knowledge, which integrates implementation details into the skeleton,
grounding the LLM's abstract outputs in practical robot contexts. Our
experiments conducted on the VirtualHome platform demonstrate CLMASP's
efficacy. Compared to the baseline executable rate of under 2% with LLM
approaches, CLMASP significantly improves this to over 90%.
