EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems

EmbodiedGovBench addresses a significant white space in robotics: the transition from 'can it do the task?' to 'is it safe and manageable in a production environment?'. While current benchmarks like ManiSkill or RLBench focus on manipulation success, this project introduces metrics for governability, audit trails, and recovery. Despite the 0-star count (due to being only 1 day old), the 5 forks suggest immediate interest from research peers likely associated with the paper release. The defensibility is currently low (3) because the value of a benchmark is entirely dependent on community adoption and becoming a 'standard'; without that, it is merely a reproducible research artifact. Frontier labs are unlikely to build this specifically as they are focused on performance scaling, but they are highly likely to *consume* such a benchmark if it gains academic or industrial consensus. The primary threat is from established robotics platforms (NVIDIA Isaac, Hugging Face LeRobot) introducing their own native safety-evaluation suites, which would provide better integration than a standalone research framework. Its displacement horizon is 1-2 years, as the field of Embodied AI is moving rapidly toward standardization of evaluation protocols.