  Object-oriented embodied navigation aims to locate specific objects, defined
by category or depicted in images. Existing methods often struggle to
generalize to open vocabulary goals without extensive training data. While
recent advances in Vision-Language Models (VLMs) offer a promising solution by
extending object recognition beyond predefined categories, efficient
goal-oriented exploration becomes more challenging in an open vocabulary
setting. We introduce OVExp, a learning-based framework that integrates VLMs
for Open-Vocabulary Exploration. OVExp constructs scene representations by
encoding observations with VLMs and projecting them onto top-down maps for
goal-conditioned exploration. Goals are encoded in the same VLM feature space,
and a lightweight transformer-based decoder predicts target locations while
maintaining versatile representation abilities. To address the impracticality
of fusing dense pixel embeddings with full 3D scene reconstruction for
training, we propose constructing maps using low-cost semantic categories and
transforming them into CLIP's embedding space via the text encoder. The simple
but effective design of OVExp significantly reduces computational costs and
demonstrates strong generalization abilities to various navigation settings.
Experiments on established benchmarks show OVExp outperforms previous zero-shot
methods, can generalize to diverse scenes, and handle different goal
modalities.
