Fleet learning is the idea that robots improve as a group. One robot encounters a new situation, the data is reviewed or used for training, and the resulting improvement is pushed back to many robots.
This is one reason deployment matters. A company with many robots operating in real environments can collect edge cases that a lab will never generate. Stairs, lighting, packaging, human behavior, floor clutter, and weird object shapes all become training data.
Fleet learning is not automatic. Data has to be captured, labeled or filtered, turned into model or policy improvements, tested safely, and deployed without making the fleet worse. The pipeline matters as much as the slogan.
When a company claims fleet learning, ask how many robots are actually in the field, what kind of data they collect, whether customers permit that collection, and whether the company has shown measurable improvement over time.