GLaMP: A Grounded Language Model-Based Multi-Agent System for Long-Horizon Robotic Task Planning in Industrial Settings

Abstract

This paper presents GLaMP, a grounded language model-based multi-agent framework for long-horizon robotic task planning in industrial environments. A key challenge in such tasks lies in the gap between high-level language reasoning and low-level perceptual grounding, which often leads to error accumulation during execution. GLaMP introduces a closed-loop architecture where a vision-language model extracts hierarchical task structures from manuals, a perception module grounds multimodal observations into symbolic predicates, and a large language model generates executable behavior trees. Through bidirectional feedback between perception and planning, the system continuously verifies and updates symbolic states, improving robustness in long-horizon execution. Preliminary experiments on representative industrial tasks demonstrate improved task success rates and reliability compared to existing approaches, highlighting the effectiveness of closed-loop grounding for robotic task planning.