ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution

TL;DR

ConceptAgent enhances robotic task planning in unstructured environments by integrating predicate grounding and LLM-guided tree search, significantly improving task completion rates and robustness against LLM hallucinations.

Contribution

The paper introduces ConceptAgent, a novel LLM-driven robotic platform that incorporates predicate grounding and embodied tree search to improve planning reliability in complex, open-world environments.

Findings

19% task completion in simulation experiments

Outperforms state-of-the-art LLM reasoning baselines

20% increase in task success with full system ablation

Abstract

Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, offer promising solutions to these challenges, as the common sense reasoning capabilities of LLMs provide a strong heuristic for efficiently searching the action space. However, prior work fails to address the possibility of hallucinations from LLMs, which results in failures to execute the planned actions largely due to logical fallacies at high- or low-levels. To contend with automation failure due to such hallucinations, we introduce ConceptAgent, a natural language-driven robotic platform designed for task execution in unstructured environments. With a focus on scalability and reliability of LLM-based planning in complex state and action spaces, we present innovations designed to limit these shortcomings, including 1) Predicate Grounding to prevent and recover from infeasible actions, and 2) an embodied version of LLM-guided Monte Carlo Tree Search with self reflection. In simulation experiments, ConceptAgent achieved a 19% task completion rate across three room layouts and 30 easy level embodied tasks outperforming other state-of-the-art LLM-driven reasoning baselines that scored 10.26% and 8.11% on the same benchmark. Additionally, ablation studies on moderate to hard embodied tasks revealed a 20% increase in task completion from the baseline agent to the fully enhanced ConceptAgent, highlighting the individual and combined contributions of Predicate Grounding and LLM-guided Tree Search to enable more robust automation in complex state and action spaces.