Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation

• URL: http://arxiv.org/abs/2508.09423v2
• Date: Tue, 21 Oct 2025 03:50:12 GMT
• Title: Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation
• Authors: Badi Li, Ren-jie Lu, Yu Zhou, Jingke Meng, Wei-shi Zheng,
• Abstract summary: Object Goal Navigation (ObjectNav) task challenges agents to locate a specified object in an unseen environment by imagining unobserved regions of the scene.<n>We propose GOAL, a generative flow-based framework that models the semantic distribution of indoor environments by bridging observed regions with full-scene semantic maps.<n>Experiments demonstrate that GOAL achieves state-of-the-art performance on MP3D and Gibson, and shows strong generalization in transfer settings to HM3D.
• Score: 47.29062082245034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Object Goal Navigation (ObjectNav) task challenges agents to locate a specified object in an unseen environment by imagining unobserved regions of the scene. Prior approaches rely on deterministic and discriminative models to complete semantic maps, overlooking the inherent uncertainty in indoor layouts and limiting their ability to generalize to unseen environments. In this work, we propose GOAL, a generative flow-based framework that models the semantic distribution of indoor environments by bridging observed regions with LLM-enriched full-scene semantic maps. During training, spatial priors inferred from large language models (LLMs) are encoded as two-dimensional Gaussian fields and injected into target maps, distilling rich contextual knowledge into the flow model and enabling more generalizable completions. Extensive experiments demonstrate that GOAL achieves state-of-the-art performance on MP3D and Gibson, and shows strong generalization in transfer settings to HM3D. Codes and pretrained models are available at https://github.com/Badi-Li/GOAL.

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