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This one as I read it is a bit different, the Physical Intelligence API requires finetuning for the task/robot while the Dream-1 model offers an API that can do zero-shot (with great limitations of course but still cool).

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If you look at what they actually demonstrate, zero-shot embodiment generalization is limited to controlling two-finger grippers on two stationary arms. The embodiments are all very similar, and when they're slightly different (even just replacing two fingers with five fingers) they can't do zero-shot anymore. But there's no real reason to do zero-shot embodiment generalization in practice, as it's trivial to collect a small amount of data on a new embodiment to get significantly improved performance. Zero-shot embodiment generalization, especially in such a narrow domain of similar configurations, is not an important problem to solve and doesn't necessarily indicate progress on the important problems.

Zero-shot task generalization is more interesting, but has been demonstrated by many other companies at this point, so it's not new. And it only becomes useful with high success rates on long-duration tasks, so demonstrating short zero-shot tasks with low success rates in toy environments is uninteresting.

What's hard and still unsolved in general-purpose robotics is doing real useful tasks end-to-end in real uncontrolled environments with high reliability. I guess there's just a lack of understanding generally about what's actually hard and useful in robotics.

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