But as you're a fellow Robot Learning (BC/LfD I'm assuming) practitioner, in the field of BC (Not IRL) what have been the more robust method for your case?
I mean two different Aloha setups (2 x 2x WidowX) in the same lab. This is the very minimal level of "generalization" you may expect, barely enough to be called generalization, but still, the learned policy seems to overfit to the individual robot's joint space quirks.
> in the field of BC (Not IRL) what have been the more robust method for your case?
Different things for different definitions of robust. What's your definition?
It's been interesting, for sure! More robust than ACT as long as you are not using action chunking (has the same problem as ACT). Downside is, it can be too slow/require too much data to train. I have a sneaking suspicion that at that scale of data even visual nearest neighbor stuff would be similarly robust.
Of course, no one has figured out an iota of useful generalization, which is sad across the field.