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Wow. This is crazy. A developer trained an AI agent in simulation and deployed it onto a real robotic air hockey table using reinforcement learning. This robot can track the puck with millimeter-level accuracy and react in roughly 20 milliseconds, fast enough to challenge even skilled human players. We’re... show more

SciTech Era

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1,585,680 次观看 • 4 个月前 •via X (Twitter)

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33 条评论

wackfpv 的头像

wackfpv4 个月前

My toxic trait is believing I can beat it

Dmitry Grinberg 的头像

Dmitry Grinberg4 个月前

This is the dumbest possible timeline. You can make a machine to play air hockey better than any human using a small eight bit micro controller. Throwing gigabytes of RAM and “ML” at it is peak idiocy.

Michael Beckerman 的头像

Michael Beckerman4 个月前

Great. Now, put TWO of those on a table so we can watch them play each other!

SciTech Era 的头像

SciTech Era4 个月前

more detail from GitHub report: for yt video explanation:

SciTech Era 的头像

SciTech Era4 个月前

Here's the system overview and a video of an agent playing against itself in simulation.

Hotep Jesus 的头像

Hotep Jesus4 个月前

Burn it now before it’s too late!!!

Again? 的头像

Again?4 个月前

For some reason, probably because I’m dense, this impresses me more than most robotics demonstrations.

Edward Smith 的头像

Edward Smith4 个月前

A big part of playing this game is watching the arm of the opposing player. The brief instant of out stretch hand gesture is enough to prepare you for a directional movement. Removing that from the game gives the person a massive disadvantage. In addition removing the human interaction and enjoyment of playing a game with others.

Captain Aaron 的头像

Captain Aaron4 个月前

And kudos it doesn’t pick up the paddle and place it on top of the puck to stop it like every human asshat I’ve ever played.

Correcting the Record with Guillotines ☭ 的头像

Correcting the Record with Guillotines ☭4 个月前

I wouldn’t call this guy “skilled,” but is it impossible to score on this thing at all? What’s the point?

William Ruider 的头像

William Ruider4 个月前

Wow, I have to say, this is one of the most useful machine learning skills I've seen lately. I experiment with Cartesian robots myself, and I know how much work you've put into this project. Integrating the entire environment, not to mention real-time feedback, hundreds of hours of simulation, and the ability to recognise and respond to simulated and intended movements in real time, a book could be written about it. You've caught my attention, and I'm subscribing to your YouTube channel. Good luck with your future projects.💪💪💪

Rohin Hattiangadi 的头像

Rohin Hattiangadi4 个月前

The psychological barrier of seeing that silver piece of metal across the table certainly can’t help things!

DenisQuaalude 的头像

DenisQuaalude4 个月前

Not crazy at all. Kinda cool, but fully expected. This ain’t nothing compare to what is coming.

A Horse with No Name 的头像

A Horse with No Name4 个月前

Wow, and they also managed to reduce the pixelation from the previous generation!

FocalZero 的头像

FocalZero4 个月前

Wow, they beat us at chess, at Go, and now in arcades...

abe kazoo 的头像

abe kazoo4 个月前

yeah, this kind of computer learning is gonna be a really big deal thirty years ago! I'm excited for the past

HeyGen 的头像

HeyGen4 个月前

20 milliseconds reaction time. You are not beating this thing. Ever.

X 的头像

X4 个月前

Nothing new -I played that like 45 years ago.

Rich Tanenbaum 的头像

Rich Tanenbaum4 个月前

The bar is going to stop the puck no matter where it is: there are spots where it shoots when the puck is not against the part moving back and forth on the bar. And if its reaction time is fast it’s been given an unfair advantage.

arXiv Bangers 的头像

arXiv Bangers4 个月前

Get fucked humans 😂😂 Banger

The Wayne Shaw 的头像

The Wayne Shaw4 个月前

Most people fear AI taking their job not realizing AI will just make their job much more brutal to perform…

Yejun Jang 的头像

Yejun Jang4 个月前

I'd like to see a game with the air hockey world champion, whoever that is.

UBC Engineering Physics 的头像

UBC Engineering Physics4 个月前

This was developed by mutiple groups of students in the Engineering Physics Project Lab at University of British Columbia. You can find some more "fascinating" projects on our feed.

PB 的头像

PB4 个月前

And they said all that token spend hasn't shown ROI yet.

Brent P 的头像

Brent P4 个月前

I would fucken smoke that robot in air hockey

Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍 的头像

Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍4 个月前

Now make 2 play each other

Nameless investor #V3403 的头像

Nameless investor #V34034 个月前

Wow, another AI breakthrough in ruining the game people used to enjoy. How about something that is actually useful and profitable?

Peter Durfee 的头像

Peter Durfee4 个月前

In related news, we can make a robot travel far faster than Usain Bolt so there's no reason to watch the Olympics any more? Fuck this artificial shit with no connection to human reality

Joshua Hartley 的头像

Joshua Hartley4 个月前

I would buy this for $2500 easy. Probably more. Love Air Hockey but too competitive for everyone else. Would play this thing just to keep the reacts quick.

Windy 的头像

Windy4 个月前

I think you'd call that machine learning not AI

Thomas Cave 的头像

Thomas Cave4 个月前

20 milliseconds: Let a cat fight against him.

Chris Ferry 的头像

Chris Ferry4 个月前

It seems like it’s taking ripper shots from just before half court whereas the human can maybe barely reach that far. Cool project but it shouldn’t be able to reach further than wingspan of opponent for shots.

らく的头像

らく4 个月前

エアホッケーが一番上手い人は、日本のコメディアンのホンジャマカです。その人に勝てたら、すごいと認めます^_^

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World Labs co-founder Justin Johnson predicts a real-to-sim-to-real future where Atlas could train a robot for any new environment in minutes using just 5 photos from your phone: "One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate the robot once how a task should be performed, and that's enough for the robot to figure out." "Another version is real to sim to real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment, like this studio, this table, moving this microphone around." "There's a version where you could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space." "If you could lower the barrier to doing that, you could do it in five minutes to take five photos, upload it to Atlas, have it generate a simulation, maybe describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot maybe in the span of a couple minutes that could come in and is perfectly adapted to this space." World Labs

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Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

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