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The problem with ACT is very similar to the problem with MPC: if your forward model of the world is not great, it will come back to bite you. So much so that af

The problem with ACT is very similar to the problem with MPC: if your forward model of the world is not great, it will come back to bite you. So much so that afaik ACT policies can have difficulty transferring between two instances of the same robot.

NalNezumi 3y ago
I don't think ACT (unlike MPC) have an explicit use case for different embodiment. Or what do you mean by two different instances? Two different tasks /arm/robots? Their recent work (Octo policy or something) wasn't even that convincing in that evaluation. 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?
- polygamous_bat 3y ago
  > I don't think ACT (unlike MPC) have an explicit use case for different embodiment. Or what do you mean by two different instances? 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?
alsodumb 3y ago
You're absolutely right about ACT. What are your thoughts on diffusion policy line of work? In my personal experience, I found it way more robust than ACT. Have you had a chance to try it?
- polygamous_bat 3y ago
  > What are your thoughts on diffusion policy line of work? 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.

NalNezumi 3y ago

I don't think ACT (unlike MPC) have an explicit use case for different embodiment. Or what do you mean by two different instances? Two different tasks /arm/robots? Their recent work (Octo policy or something) wasn't even that convincing in that evaluation. 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?

polygamous_bat 3y ago
> I don't think ACT (unlike MPC) have an explicit use case for different embodiment. Or what do you mean by two different instances? 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?

alsodumb 3y ago

You're absolutely right about ACT. What are your thoughts on diffusion policy line of work? In my personal experience, I found it way more robust than ACT. Have you had a chance to try it?

polygamous_bat 3y ago
> What are your thoughts on diffusion policy line of work? 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.