This post didn't really convince me that robotics is inherently harder than generating text or images On the one hand we have problems where ~7B humans have be

This post didn't really convince me that robotics is inherently harder than generating text or images On the one hand we have problems where ~7B humans have been generating data for 30 years every day (more if you count old books), on the other hand we have a problem where researcher are working with ~1000 human collected trajectories (I think the largest existing dataset is OXE with ~1M trajectories: https://robotics-transformer-x.github.io/ https://robotics-transformer-x.github.io/ ) Web-scale datasets for LLMs benefits from a natural diversity, they're not highly correlated samples generated by contractors or researchers in academic labs. In the largest OXE dataset, what do you think is the likelihood that there is a sample where a robot picks up a rock from the ground and throws it in a lake? Close to zero, because tele-operated data comes from a very constrained data distribution. Another problem is that robotics doesn't have an easy universal representation for its data. Let's say we were able to collect web-scale dataset for one particular robot A with high diversity, how would it transfer to robot B with a slightly different design? Probably poorly, so not only does the data distribution needs to cover a high range of behavior, it must also cover a high range of embodiment/hardware With that being said, I think it's fair to say that collecting large scale dataset for general robotics is much harder than collecting text or images (at least in the current state of humanity)