VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
VANDERER is a novel exploration framework for mobile agents that uses only monocular camera data, leveraging a Visual Curiosity Module to guide pre-trained diffusion policies. It outperforms NoMaD by 13.4% in explored area across simulated environments and reveals a correlation between visual and geometric curiosity in outdoor settings.
[2606.14879] VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
[Submitted on 12 Jun 2026]
Title:VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
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Abstract:Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks. Traditional methods rely on generating occupancy maps and optimizing the sequence in which unexplored regions are visited. However, in sensor-constrained settings, such as those limited to monocular cameras, generating accurate occupancy maps is challenging. To address this, we propose VANDERER, an exploration framework that leverages a Visual Curiosity Module (VCM) to guide pre-trained diffusion policies using only monocular image data. This curiosity module predicts the outcomes of proposed actions via a navigation world model and evaluates them through a curiosity cost. The cost then guides the diffusion process toward generating actions that maximize exploration. Evaluated across diverse simulated environments, VANDERER consistently outperforms established baselines, exploring an average of 13.4% more area than NoMaD. Our results reveal a direct correlation between visual and geometric curiosity in outdoor environments, demonstrating that VANDERER can effectively leverage this relationship for efficient exploration using sensor-constrained agents.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2606.14879 [cs.RO]
(or arXiv:2606.14879v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14879
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Venkata Naren Devarakonda [view email] [v1] Fri, 12 Jun 2026 18:33:12 UTC (12,071 KB)
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