Andy Zeng

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@andyzengineer

Co-founder @GeneralistAI general intelligence born from the physical world
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andyzeng.github.io Joined September 2017

660 Following 11K Followers 524 Posts

Latest posts 5

Andy Zeng @andyzengineer 6d

the models are getting better at the nitty gritty

Generalist Verified account @GeneralistAI 6d

Even thinner O-rings.

These tasks test dexterity and the ability of the models to perceive fine-grained millimeter-scale details.

Read more about GEN-1.5 and sign up for the waitlist in the comments below.

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Andy Zeng @andyzengineer Oct 20

if you look closely at the reliability test later in the video,

you’ll see a few examples of “physical reasoning” -- it tries something, it doesn’t fit (because physics), so it backtracks, switches gears, then does something entirely different to reach the goal.

in this case, it’s taking out the stuck puck, making space elsewhere, and putting it back in.

things like this (… a sort of physical problem-solving with step-by-step “chain-of-actions”) should just work with models out of the box.

0:06

Generalist Verified account @GeneralistAI Oct 20

How many pucks could a puck bot pack if a puck bot could pack pucks? 🏒

12 hockey pucks is a tight fit in this container – watch the model fix the arrangement when pucks get stuck or misplaced.

GEN-1.5 doesn't just puck around. Read more about it in our blog post and sign up for the waitlist in the comments below.

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Andy Zeng @andyzengineer Oct 6

post-training to hill-climb dexterity

Generalist Verified account @GeneralistAI Oct 6

Robot puts a ring on it

GEN-1.5 puts an O-ring on a hydraulic rod gland, then takes the ring off, then puts the ring back on, then takes the ring off, then ring on, ring off, ring on...

Read more about GEN-1.5 in our blog post in the comments below.

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Andy Zeng @andyzengineer Aug 4

The models are getting better everyday.

This is a pretty contact-rich task, and forces us to reckon with the fact that our policies not only need to learn the quirks of different robots, but also have to do it in a way that generalizes to the fine-grained subtleties of friction and forces e.g. so that objects don’t get wedged or stuck.

Gave me goosebumps when I first saw these results internally. Something about it too, that just makes these models feel just a bit more “human” now in the way they move than they did before. Wild times.

0:46

Generalist Verified account @GeneralistAI Aug 4

We've improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board.

Read more about GEN-1 in our blog posts in the comments below.

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Andy Zeng @andyzengineer May 12

Decades of hardware development led to strong, fast, and precise robot arms. The moment we can put in general intelligence in these things, we can leverage the full spectra of capabilities that they were always meant to capture.

We’re betting on a future where robot hardware will continue to improve, and we intend to build the best models on top of the best hardware to push the frontier of capabilities and reliability.

Here’s an old video from TossingBot that I think helps make this point extra clear. Industrial-grade repeatability started out as a crutch for dumb software -- but if you pair it with the right AI models, then it becomes an advantage that is superhuman. There are factories where the same UR robot arms are still being used to precisely and repeatably build the same car parts, operating for 10 yrs straight without a single failure or shutdown.

Humanoids will get there too (among other form factors). Not yet today, but eventually. And our models will be ready to meet them when they do.

0:29

Generalist Verified account @GeneralistAI May 12

GEN-1 delicately arranges potato chips, and lifts a heavy bag of potatoes — from a gentle touch to a strong grip.

Read more about Gen-1 in our blog posts in the comments below ↓

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