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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...

1,585,680 Aufrufe • vor 4 Monaten •via X (Twitter)

33 Kommentare

Profilbild von wackfpv
wackfpvvor 4 Monaten

My toxic trait is believing I can beat it

Profilbild von Dmitry Grinberg
Dmitry Grinbergvor 4 Monaten

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.

Profilbild von Michael Beckerman
Michael Beckermanvor 4 Monaten

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

Profilbild von SciTech Era
SciTech Eravor 4 Monaten

more detail from GitHub report: for yt video explanation:

Profilbild von SciTech Era
SciTech Eravor 4 Monaten

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

Profilbild von Hotep Jesus
Hotep Jesusvor 4 Monaten

Burn it now before it’s too late!!!

Profilbild von Again?
Again?vor 4 Monaten

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

Profilbild von Edward Smith
Edward Smithvor 4 Monaten

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.

Profilbild von Captain Aaron
Captain Aaronvor 4 Monaten

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.

Profilbild von Correcting the Record with Guillotines ☭
Correcting the Record with Guillotines ☭vor 4 Monaten

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

Profilbild von William Ruider
William Ruidervor 4 Monaten

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.💪💪💪

Profilbild von Rohin Hattiangadi
Rohin Hattiangadivor 4 Monaten

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

Profilbild von DenisQuaalude
DenisQuaaludevor 4 Monaten

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

Profilbild von A Horse with No Name
A Horse with No Namevor 4 Monaten

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

Profilbild von FocalZero
FocalZerovor 4 Monaten

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

Profilbild von abe kazoo
abe kazoovor 4 Monaten

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

Profilbild von HeyGen
HeyGenvor 4 Monaten

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

Profilbild von X
Xvor 4 Monaten

Nothing new -I played that like 45 years ago.

Profilbild von Rich Tanenbaum
Rich Tanenbaumvor 4 Monaten

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.

Profilbild von arXiv Bangers
arXiv Bangersvor 4 Monaten

Get fucked humans 😂😂 Banger

Profilbild von The Wayne Shaw
The Wayne Shawvor 4 Monaten

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

Profilbild von Yejun Jang
Yejun Jangvor 4 Monaten

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

Profilbild von UBC Engineering Physics
UBC Engineering Physicsvor 4 Monaten

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.

Profilbild von PB
PBvor 4 Monaten

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

Profilbild von Brent P
Brent Pvor 4 Monaten

I would fucken smoke that robot in air hockey

Profilbild von Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍
Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍vor 4 Monaten

Now make 2 play each other

Profilbild von Nameless investor #V3403
Nameless investor #V3403vor 4 Monaten

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

Profilbild von Peter Durfee
Peter Durfeevor 4 Monaten

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

Profilbild von Joshua Hartley
Joshua Hartleyvor 4 Monaten

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.

Profilbild von Windy
Windyvor 4 Monaten

I think you'd call that machine learning not AI

Profilbild von Thomas Cave
Thomas Cavevor 4 Monaten

20 milliseconds: Let a cat fight against him.

Profilbild von Chris Ferry
Chris Ferryvor 4 Monaten

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.

Profilbild von らく
らくvor 4 Monaten

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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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 Aufrufe • vor 10 Monaten