Recently, I have seen papers about large datasets for robotics such as DROID(https://droid-dataset.github.io/) or Open X-Embodiment(https://robotics-transformer-x.github.io/).

As I see, the datasets are specific to some types of robots(although X-Embodiment allows one robot to learn from another robot's data) and environments. If one wants to add another robot into the dataset, they have to do all data sampling again, which is quite expensive. Some environments might be difficult to reproduce, especially as they collected data from all the labs in the world.

I am wondering: why don't they instead set up data collection procedure on simulation? it will make the data collection way cheaper. When they want to add a new robot and collect data with the same tasks and environments like other robots, they can do it easily. It is also easy to add a new task and collect data from all robots/environments. Then, why they collect data in real world while giving up on such reproducibility/extensibility? Is Sim2Real that bad, even if it can collect way more samples easily?