How did a $100m ARR robotics company solve scale? "Unlike language models, speech or video, in robotics there are three axis to evaluate data quality. One is how scalable you are, how diverse it is, and how close it is to robot." Robotics has four ways to collect training data. Each one comes with its own fault. Robots collecting their own data produces the best-targeted data and scales slowest, because the physical world will not run faster than real time. Teleoperation is the industry default and produces almost no diversity. Simulation runs far faster than real time, but every new task needs an environment built by hand. Human video is the most abundant and the furthest from a robot, since a person has a different body and you cannot see the forces they apply. There is not one "Golden path" as @deepakpathak put it on stage at AUTONOMOUS earlier this year. @SkildAI