[sterlind]

I don't think the issue with robotics is a data gap. maybe somewhat, but the real issues are that:

- RL is extraordinarily sample-inefficient.

- distribution shift/catastrophic forgetting aren't solved. only off-policy learning with giant decorrelated batches works.

- the breakout success of transformers as an architecture doesn't neatly translate to robot motion policy models.

the field is missing fundamental breakthroughs.

I also find it very interesting that conversational AI has taken this long. where are the models with good turn-taking? passive listening? the ability not to respond in paragraphs? has Anthropic simply not gotten around to it?

[aspenmartin]

All of these points are great. The first one motivates world models which lots of labs work on. Not many people tend to understand the strategic value of those “open world” or interactive generation models: its robotics and planning. But also like you say you’re right, there are complicated problems to solve and it’s not totally clear the timeline. But where there’s data and compute, there’s a way.

For conversational AI these labs do have lots of things to do lol but you’re right; it likely also requires some architectural improvements but you see the infancy: look at the llama4 speech duplex model. Very unimpressive yet all of the components are there. Just a matter of pushing on them, licensing and commissioning better data, etc. takes time and compute is stretched thin.