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AI agents can learn to climb leaderboards in complex adversarial games by refining executable policies, but struggle with intricate rule sets
RoboDawn is investigated, a human-intuitive interface that exposes robotic control to an agentic VLM through a compact set of discrete translation, rotation, and gripper commands, and an in-context learning (ICL) scheme is introduced that ground the VLM in both interface usage and task-solving strategies.
