Sim-to-real is an organizational-learning problem
The Defense Advanced Research Projects Agency (DARPA) is seeking proposals for Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT). The program challenges a common assumption: instead of building ever more detailed simulations, diverse lower-fidelity environments may help autonomous systems learn concepts that transfer more readily to unfamiliar real-world settings.
The technical hypothesis is provocative. The management lesson is familiar: a model becomes dangerous when an organization forgets which parts of reality it left out.