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%.

Unfortunately, by using such heuristics, it is not possible to solve any reasonably complex set of problems in a reasonably complex domain. Regardless of how good such heuristics are at directing search, attempts to traverse a complex problem space can be caught in a combinatorial quagmire. This paper presents an approach to augmenting the power of the heuristic search process. The essence of this approach is to utilize a means for discriminating between important information and details in the problem space. By planning in a hierarchy of abstraction spaces in which successive levels of detail are introduced, significant increases in problem-solving power have been achieved. Section II sketches the hierarchical planning approach and gives motivation for its use. Sections III and IV describe the definition and use of abstraction spaces by ABSTRIPS (Abstraction-Based STRIPS), a modification of the STRIPS problem-solving system that incorporates this approach. Section V describes the performance of the system.