I wonder to what degree that holds up for very limited domains. You have to balance model size, power budget and cost which is a completely different ballgame than looking at a stationary server with a near infinite power budget and where the costs can be amortized over much larger numbers of hits.

Robotics tends to be at the worst possible intersection point of all three of those: limited power budget, relatively small model size and cheap enough to be affordable when embedded into an appliance.

Training specialized models for every task is a "general method leveraging computation" in the sense of the Bitter Lesson. Making the best possible model for each task is guaranteed to be at least as good as making one model and using it for everything, and often it'll be better.