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GuardedAct is proposed, a sandbox-first remediation framework that interposes a blast-radius-aware verification layer between the LLM action generator and the production environment and achieves an overall recovery rate and a modest sandbox-induced increase in mean time to recovery.
Achieving robust sim-to-real transfer, this framework allows robots to learn dexterous skills directly from human videos, outperforming traditional methods.
