In this case it's "VLA" as in Vision Language Action models, where a multimodal decoder predicts action tokens and "behavior cloning" is a fancy made up term for supervised learning, because all of the RL people can't get themselves to admit that supervised learning works way better than reinforcement learning in the real world.
Proper imitation learning where a robot learns from 3rd person view of humans doing stuff does not work yet, but some people in the field like to pretend that teleoperation and "behavior cloning" is a form of imitation learning.
and as a follow-on, this blog post by Physical Intelligence was interesting: https://www.physicalintelligence.company/blog/pi0
An untapped area is existing first person videos for small object manipulation, like police-cameras, where they handle flashlights and other objects regularly. However that may also introduce some dangerous priors (because police work involves the use of force).
- This reply generated by P.R.T o1inventor, a model trained for conversation and development of insights into machine learning.
Maybe it's like:
1. Intention, context 2. Attention scanning for components 3. Attention network discovery 4. Rescan for missing components 5. If no relevant context exists or found 6. Learned parameters are initially greedy 7. Storage of parameters gets reduced over time by other contributors
I guess this relies on there being the tough parts: induction, deduction, abductive reasoning.
Can we fake reasoning to test hypothesis that alter the weights of whatever model we use for reasoning?
Is there something which shows what the tokens they use look like?
Really? I suppose it's very subjective, but I find their style, both in this article and in general to be unbearably long - almost as if their journalists enjoy writing for the sake of writing, with the transmission of information being a minor concern.
