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We accidentally solved robotics by watching 1M hours of YouTube

(ksagar.bearblog.dev)
209 points alexcos | 2 comments | 29 Jun 25 16:08 UTC | HN request time: 0.412s | source
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liendolucas ◴[30 Jun 25 09:25 UTC] No.44421182[source]▶
I didn't understand a single word about this post and what was supposed to be solved and had to stop reading.

Was this actually written by a human being? If so, the author(s) suffer from severe language communication problems. Doesn't seem to be grounded at least with reality and my personal experience with robotics. But here's my real world take:

Robotics is going to be partially solved when ROS/ROS2 becomes effectively exterminated and completely replaced by a sane robotics framework.

I seriously urge the authors to use ROS/ROS2. Show us, implementing your solution with ROS, pushing it to a repository and allow others to verify what you solved, maybe?. Suffer a bit with the framework and then write a real post about real robotics hands-on, and not just wander on fancy uncomprehensible stuff that probably no-one will ever do.

Then we can maybe start talking about robotics.

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1. w4 ◴[30 Jun 25 13:38 UTC] No.44423136[source]▶
It is readily understandable if you are fluent in the jargon surrounding state of the art LLMs and deep learning. It’s completely inscrutable if you aren’t. The article is also very high level and disconnected from specifics. You can skip to FAIR’s paper and code (linked at the article’s end) for specifics: https://github.com/facebookresearch/vjepa2

If I had to guess, it seems likely that there will be a serious cultural disconnect as 20-something deep learning researchers increasingly move into robotics, not unlike the cultural disconnect that happened in natural language processing in the 2010s and early 20s. Probably lots of interesting developments, and also lots of youngsters excitedly reinventing things that were solved decades ago.

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2. godelski ◴[30 Jun 25 20:12 UTC] No.44427335[source]▶

  > if you are fluent in the jargon surrounding state of the art LLMs and deep learning
It is definitely not following that jargon. Maybe it follows the tech influencer blog post jargon but I can definitively say it doesn't follow jargon used in research. Which, they are summarizing a research paper. Consequently they misinterpret things and use weird phrases like "actionable physics," which is self referential. "A" physics model is necessarily actionable. It is required to be a counterfactual model. While I can understand the rephrasing to clarify to a more general audience that's a completely different thing than "being fluent in SOTA work." It's literally the opposite...

Also, it definitely doesn't help that they remove all capitalization except in nouns.