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AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation
As AI agents become increasingly capable, agent-driven robotic control is emerging as a compelling paradigm. However, prevailing vision-language-action (VLA) models and world action models (WAMs) still rely on natural-language instructions to specify manipulation tasks, an ill-suited interface for a
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