G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal Navigation
In this paper, we propose the Geometry Grounded World Action Model (G$^2$WAM) for image-goal navigation.
ProofPaper ↗
Key points
- World models capture environment dynamics and support counterfactual reasoning for planning.
- In image-goal navigation, where the target often lies beyond the current field of view, the agent must not only model environmental dynamics but also maintain a persistent spatial memory of the scene.
- G$^2$WAM encodes spatial cues through a learned scene-coordinate localization module that anchors the current, goal, and history frames in a common reference frame, yielding a stable and consistent spatial context that enables efficient navigation without explicit 3D reconstruction.
- Following the world-action-model paradigm, G$^2$WAM jointly predicts future states and actions, eliminating the need for costly cross-entropy method (CEM) style planning.
Sources (1)
- [1]G$^2$WAM: World Action Models for Efficient and Geometry Grounded Image-Goal NavigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 10:36 AM
In this paper, we propose the Geometry Grounded World Action Model (G$^2$WAM) for image-goal navigation.
World models capture environment dynamics and support counterfactual reasoning for planning.
Extractive summary: sentences quoted from the sources.
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