The real-world performance of a robot often depends less on how advanced the algorithm model is and more on whether the training data is grounded enough in reality. A common problem is that things work perfectly in the lab but fail on site — the lighting changes, the background gets cluttered, or the target object switches to a different model, and the recognition rate drops immediately. Behind this is usually insufficient data coverage: the collection process didn't fully account for what the real scenario actually looks like.
To solve this, designing a data collection plan cannot focus only on "quantity" — it must also consider "distribution." The first step is to break down the scenario variables. What lighting conditions, angles, occlusions, and surface materials will the robot encounter in actual use? List these variables, such as indoor vs. outdoor, day vs. night, presence or absence of reflections, and the degree of wear on objects. This way, the collection task is no longer about taking random photos or videos but about covering a matrix of variables.
The second step is to introduce "edge cases." Many teams are accustomed to collecting "standard situations" — facing the camera directly, uniform lighting, and complete targets. But the real difficulty in the real world lies precisely at the edges: half a face blocked, parts stained with grease, labels with curled corners, or light shining directly from behind. These samples may not look pretty, but they are key to improving robustness. When designing the collection plan, deliberately create these "imperfect" conditions, and even collect extreme conditions as separate batches.
The third step is to supplement with dynamic data. The changes in viewpoint, speed, and motion blur during a robot's movement are completely different from static posed shots. You need to design a motion collection process — for example, having the robot approach the target at different speeds and recording imaging characteristics at different distances. If conditions allow, use multiple devices to record simultaneously, which makes it easier to compare viewpoints later.
Finally, the implementation of the collection plan relies on process constraints. Since everyone has different operating habits, certain variable combinations can easily be missed. Use a ticket system or a simple record sheet to ensure that each variable combination has a corresponding sample size. At the same time, conduct regular "blind tests" — run the newly collected data through the existing model to see which scenarios it still fails to handle, and then supplement the collection tasks accordingly.
In the end, the root cause of insufficient data coverage is "not imagining the details concretely enough." The value of a plan lies not in piling up hours but in turning every possible deviation in each step into a data problem, so that before a robot truly goes into the field, it has already seen enough of the imperfect world.