robots0only

6 ms·

1.
by robots0only 1mo ago
I work at GDM and this is not at all true, 3.7 is markedly different (and better) than 3.6.
2.
by robots0only 4mo ago
Any robot that does this reliably is easily more than a decade away.
3.
by robots0only 6mo ago
From your early point -- both 1) and 2) are true. True human level dexterity is ver far (few decades surely), it would require further advancements in hardware, learning approaches etc. Recent approaches provide a glimmer of hope and maybe
4.
by robots0only 7mo ago
here is a real video of a unitree robot playing ping pong https://www.youtube.com/watch?v=tOfPKW6D3gE
5.
by robots0only 9mo ago
how do you know this is a better model? I wouldn't take any of the numbers at face value especially when all they have done is more/better post-training and thus the base pre-trained model capabilities is still the same. The mode
6.
by robots0only 9mo ago
This is probably very similar to what happened!
7.
by robots0only 10mo ago
the problem here is that text as the communication interface is not good for this. the model should be reasoning in the pose space (and generally in more geometric spaces), then interpolation and drawing is pretty easy. I think this will ha
8.
by robots0only 11mo ago
How are you defining dextrous? I think it can be somewhat challenging but not dextrous -- the robot doesn't need to be very precise (few cms here and there do not matter), there are no forces involved, motions are all pick-place. Dextr
9.
by robots0only 11mo ago
Locomotion and manipulation are pretty different. The former we know how to do well -- this is what you see in unitree videos. Manipulation still not so much. This is not at all like GPT-2 because we still don't know what to scale (and
10.
by robots0only 11mo ago
Here you can see another much simpler robot folding clothes for far longer: https://www.youtube.com/watch?v=gdeBIR0jVvU (there are more videos from other companies as well) To answer your question -- folding clothes is easy
11.
by robots0only 11mo ago
+100!!! Please don't fall for the HYPE. The current best neural networks only have around 60% success rates for small horizon tasks (think 10-20 seconds e.g. pick up apple). That is why there is so much cut-motions in this video. The f
12.
by robots0only 1y ago
In all of these posts there is someone claiming Claude is the best, then somebody else claiming they have tried a bunch of times and for them Gemini is the best while others find GPT-5 is supreme. Obviously, all of these are subjective narr
13.
by robots0only 1y ago
so their way to differentiate against frontier labs is to try writing research blog posts (not papers). It will be interesting to see how this plays out. I don't think that anyone serious about developing frontier models would be putti
14.
by robots0only 1y ago
This paper was just too overhyped by the authors. Also, the initial evals were very limited and very strange. This blog post does a much better job at a similar observation -- goes into details and does proper evaluation (also better attrib
15.
by robots0only 1y ago
and so is the safety margin for a humanoid. The consumer market is huge only if the robots are highly reliable and work very well both of which are not true at the moment. Things will change but it will take quite a bit of time and much mor
16.
by robots0only 1y ago
Claude is extremely poor at vision when compared to Gemini and ChatGPT. i think anthropic severely overfit their evals to coding/text etc. use cases. maybe naively adding browser use would work, but I am a bit skeptical.
17.
by robots0only 1y ago
The 1 million robot number that Amazon keeps on using is a quite nuanced. It includes more ~800K robots that simply just move stuff in a 2D plane. I think the number of robots that actually manipulate things is far far less (probably less t
18.
by robots0only 1y ago
ohh wow, that's bad, just tried this with Gemini 2.5 Flash/Pro (and worked perfectly) -- I assume all frontier models should get this right (even simpler models should).