ForesightNav: Adaptive Semantic-Geometric Planning for Universal Zero-Shot Goal-Oriented Navigation

Abstract

Universal zero-shot goal-oriented navigation requires an agent to locate object categories, target instances, or text-described goals in unseen environments without task-specific policy training. Existing methods usually rely on either broad semantic exploration or fine-grained target verification, but they can become inefficient when semantic cues are sparse, ambiguous, or insufficient for reliable graph matching. This paper presents ForesightNav, a training-free universal zero-shot navigation framework that adaptively combines context-aware semantic value mapping, graph-based target verification, and TSP-based frontier ordering. Guided by semantic-score dispersion and graph-matching confidence, the planner switches among geometric exploration, semantic exploration, partial-match exploration, and target verification, balancing broad goal-directed search with fine-grained target confirmation. Experiments on MP3D, HM3D, and RoboTHOR across Object Navigation (ObjectNav), Instance Navigation (InstanceNav), and Text Navigation (TextNav) show favorable performance across different goal modalities. Compared with UniGoal, ForesightNav improves ObjectNav performance on three evaluated benchmarks, with the largest gains on RoboTHOR ObjectNav: 18.6 percentage points in Success Rate (SR) and 11.4 in Success weighted by Path Length (SPL). On HM3D, it achieves 65.3% SR on InstanceNav and 29.6% SR on TextNav, suggesting that the same training-free planning process remains applicable to instance- and language-specified goals. Foundation-model analysis and ablation results further support the effectiveness of adaptive switching, context-aware semantic mapping, and TSP-based frontier ordering. Code is available at https://anonymous.4open.science/r/ForesightNav