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Replacing a single skill in a robot's compositional policy can swing task success by up to 50%, and existing behavioral distance metrics can't predict which skill matters most.
Multi-robot coordination doesn't need to turn each robot into a fragmented multi-agent system; treating them as federated single agents actually improves governance and recovery.
Current embodied AI benchmarks overlook critical governance aspects like controllability and safety during upgrades, a gap EmbodiedGovBench directly addresses.
Robots can achieve 100% task success and maintain safety across diverse tasks by modeling them as single agents with hot-swappable capability modules governed by a policy-separated runtime.
