LLM-guided Task and Motion Planning using Knowledge-based Reasoning

TL;DR

This paper introduces Onto-LLM-TAMP, a knowledge-based reasoning framework that enhances large language model-guided task and motion planning by improving adaptability and semantic accuracy in dynamic environments.

Contribution

It presents a novel integration of knowledge-based reasoning with LLM-guided TAMP to address static prompting limitations and improve plan correctness.

Findings

Resolves semantic errors in symbolic plan generation

Improves adaptability to dynamic environments

Enhances semantic correctness of task plans

Abstract

Performing complex manipulation tasks in dynamic environments requires efficient Task and Motion Planning (TAMP) approaches that combine high-level symbolic plans with low-level motion control. Advances in Large Language Models (LLMs), such as GPT-4, are transforming task planning by offering natural language as an intuitive and flexible way to describe tasks, generate symbolic plans, and reason. However, the effectiveness of LLM-based TAMP approaches is limited due to static and template-based prompting, which limits adaptability to dynamic environments and complex task contexts. To address these limitations, this work proposes a novel Onto-LLM-TAMP framework that employs knowledge-based reasoning to refine and expand user prompts with task-contextual reasoning and knowledge-based environment state descriptions. Integrating domain-specific knowledge into the prompt ensures semantically accurate and context-aware task plans. The proposed framework demonstrates its effectiveness by resolving semantic errors in symbolic plan generation, such as maintaining logical temporal goal ordering in scenarios involving hierarchical object placement. The proposed framework is validated through both simulation and real-world scenarios, demonstrating significant improvements over the baseline approach in terms of adaptability to dynamic environments and the generation of semantically correct task plans.