Intent Entropy: Planning Intent Uncertainty Detection and Entropy-Gated Reflection for Ambiguous Instructions

Abstract

Embodied agents executing tasks from natural language instructions face the challenge of interpreting ambiguous commands that may lead to inconsistent planning. Conventional uncertainty estimation methods, reliant on token-level probabilities or semantic embeddings, struggle to capture task-level intent divergence, while large language model (LLM) based planning suffers from computational overhead. To address these problems, this paper presents Intent Entropy, an innovative uncertainty quantification method that models planning-level ambiguity through structured task chain clustering. By computing entropy over equivalence-clustered subtask sequences, Intent Entropy directly measures intent uncertainty rather than linguistic variations, exhibiting statistically significant negative correlation with task success, substantially outperforming conventional uncertainty measures. Building upon this metric, we introduce the Entropy-Gated Reflection Mechanism (ECHO), a hierarchical framework that employs lightweight models for initial planning and selectively triggers large-model reflection only under high uncertainty. The proposed methodology is evaluated on the ALFRED benchmark, demonstrating superior performance on both seen and unseen scenarios while significantly reducing computational costs compared to baseline methods. Furthermore, ECHO exhibits robust performance across varying instruction ambiguity levels, showcasing its effectiveness in uncertainty-aware adaptive planning.