I used to think more human corrections automatically meant better robot training. Turns out, that intuition is wrong. After reading about this experiment by Axis, what struck me most wasn't the sheer volume of data collected, but rather how they filtered it. Consider a very simple task: placing a block of tofu onto a plate. When the robot was on the verge of failure, users could directly take over via the Axis Hub to make corrections. A total of 660 interventions were recorded. However, @axisrobotics didn't use all of them for training. Only 161 segments representing 24.4% of the total passed the filter. The reasoning makes perfect sense. Just because a trajectory ultimately succeeds doesn't mean every human action within it is worth learning. There might be redundant movements, circuitous paths, or pauses that offer no useful signal. Therefore, each correction had to clear three hurdles: → Successfully replay in simulation → Demonstrate that the original policy actually failed → Extract the critical segment and perform a closed-loop check to see if the robot could recover on its own This is the part I like best: The value of human feedback doesn't lie simply in the fact that "a human successfully performed the task." It lies in pinpointing exactly which action transformed a failure state into a recovery that the robot could replicate on its own. 660 corrections sounds like a huge number. But 161 verified signals are far more valuable. Perhaps this is how community-sourced data truly evolves into a scalable data engine.