Robot Learning

Fleet Learning

Fleet learning is the practice of improving robot capabilities by aggregating experience from many deployed robots, retraining shared models on the pooled data, and redistributing updated policies across the fleet. The paradigm, popularized in autonomous driving, extends to manipulation fleets where interventions, failures, and successes from the field become training data. Infrastructure concerns include data triage, automatic labeling, evaluation gating, and staged rollout of new policies.

Why it matters for physical AI

A single robot gathers experience slowly, but a fleet compounds it; the data flywheel from deployed units is the central economic argument for scaling robot foundation models in the real world.