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GuardEn (Guarding by Safety Rule Entailment), an executable safeguard framework that decomposes safety policies into atomic propositions through Safety-Rule Compilation, modeling their composition as executable code, is proposed.
Experiments demonstrate that Code-as-Auditor delivers more accurate and evidence-backed evaluations, enabling automated compliance regulation checking grounded in explicit regulatory criteria and improves the logical consistency of the generated code and the traceability of outcomes.
AutoDex accelerates dexterous grasping data collection by 4.8 times while significantly improving grasp success rates from simulation-only validation.
Achieving reliable long-horizon manipulation without prior training on specific tasks, this framework enables robots to adaptively execute complex actions in new environments based solely on language instructions.
A 48-camera system finally unlocks real-time, room-scale multi-human, multi-robot interaction research in realistic home environments.
