Embodied AI Has a $30B Problem: Nobody Knows What 'Good' Means

Short summary

Embodied AI has attracted $30B in investment but lacks shared industry standards for evaluating robot performance. Simulation benchmarks drop from 98% in testing to 10% in real-world deployment because they ignore perception noise, sensor drift, and object deformation—an information compression problem, not just model fidelity. The author proposes a four-layer verification framework to align benchmarks with actual performance.

• •$30B invested in embodied AI, but no shared standard for what 'good' performance means
• •Simulation benchmarks fail because they compress away critical real-world information: perception noise, sensor drift, object deformation
• •Four-layer verification framework (rule-following, closed-loop feedback, self-consistency, calibration) could bridge the Sim2Real gap

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