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cs.RO2026
From World Models to World Action Models: Rethinking Next-State Prediction
Tingyu Yuan, Ziming Ji, Biaoliang Guan +9
Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve into World-Action Models (WAMs)…
cs.RO2026
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Zhaopeng Gu, Bingke Zhu, Tianxi Lin +9
World Action Models~(WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon predict…
cs.RO2026
DIM-WAM: World-Action Modeling with Diverse Historical Event Memory
Kai Wang, Zhaopeng Gu, Yixiang Chen +7
World-action models have shown promising robot-manipulation performance by jointly predicting future visual states and actions. However, existing methods mainly rely on short-term…