Author

Yue Su

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Preprint Aug 2026

SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space

SG-WAM introduces learnable dynamics tokens and a Self-Guided World Predictor that forecasts their future latent states conditioned on intervening robot actions, and is a self-guided framework that learns geometry-aware action-conditioned dynamics directly in the policy-derived representation space.

Ruiteng Zhao, Zhengsheng Zhang, Yue Su et al. · 1 citation