I thought so too at first, but here's my new thinking: The most efficient way to learn real world tasks (for say a robot) is to learn in a realistic simulated

I thought so too at first, but here's my new thinking: The most efficient way to learn real world tasks (for say a robot) is to learn in a realistic simulated environment, which ideally can be controlled by the agent, e.g. rewinding state after a failure to correct that particular behaviour. Ideally the simulation is regularly calibrated against real experiments, including confidence intervals on the real and simulated sensor inputs. Given that model of learning, Uber's approach here is very reasonable.