Scene Map-based Prompt Tuning for Navigation Instruction Generation

Abstract

Navigation instruction generation (NIG), which provides interactive feedback and guidance to humans along a trajectory, is vital for developing embodied agents capable of human-machine communication and collaboration through natural language. Early data-driven methods directly map sequences of past observations to trajectory descriptions on limited datasets, lacking the necessary spatial understanding in complex 3D environments. While recent approaches leverage Large Language Models (LLMs) to improve NIG, they often overlook the global spatial context in navigation, such as the inherent space discretization in maps. Instead of straightforwardly feeding textual descriptions of the map into LLMs, we propose a scene map-based prompt tuning framework for NIG, MAPInstructor, which incorporates map context for parameter-efficient updating of LLMs. MAPInstructor comprises three key components: (i) scene representation encoding, where egocentric observations are projected into 3D voxels for fine-grained scene understanding; (ii) map prompt tuning, which integrates a topological map representation of the entire trajectory into an LLM-based decoder; and (iii) landmark uncertainty assessment, which mitigates hallucinations in landmark predictions, thereby enhancing the reliability and coherence of instruction generation. Extensive experiments on three navigation datasets (i.e., R2R, REVERIE, RxR) confirm the generalization and effectiveness of our algorithm.