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A two-step deep reinforcement learning framework that jointly learns the maximal robust RAS set and the corresponding control policy and identifies the RAS set with high accuracy while outperforming baseline methods on RAS tasks.
Scale-Plan is presented, a scalable LLM-assisted framework that generates compact, task-relevant problem representations from natural language instructions that outperforms pure LLM and hybrid LLM-PDDL baselines across all metrics, improving scalability and reliability.
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