Preprint
Sep 2026
AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation
AR-WAM is presented, a visual-conditioned, agent-ready world action model that replaces language with two complementary conditions: a visual grounding prompt and a learnable operation token dictating the atomic skill to execute, predicting scene evolution within compact latent states while decoding actions.
Yi-Cheng Jiang, Ze-Sen Gan, Xiao-Bo Wang et al.
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