WoMAP: World Models For Embodied Open-Vocabulary Object Localization

Abstract

Active object localization remains a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art robot policies either struggle to generalize beyond demonstration datasets (e.g., imitation learning methods) or fail to generate physically grounded actions (e.g., VLMs). To address these limitations, we introduce WoMAP (World Models for Active Perception): a recipe for training open-vocabulary object localization policies that: (i) uses a Gaussian Splatting-based real-to-sim-to-real pipeline for scalable data generation without the need for expert demonstrations, (ii) distills dense rewards signals from open-vocabulary object detectors, and (iii) leverages a latent world model for dynamics and rewards prediction to ground high-level action proposals at inference time. Rigorous simulation and hardware experiments demonstrate WoMAP's superior performance in a wide range of zero-shot object localization tasks, with a 63\% success rate compared to 10\%success rate compared to a VLM baseline, and only a 10 - 20\% drop in performance when directly transferring from sim to real.