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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

22,926 subscribers

1,585,680 görüntüleme • 4 ay önce •via X (Twitter)

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wackfpv profil fotoğrafı

wackfpv4 ay önce

My toxic trait is believing I can beat it

Dmitry Grinberg profil fotoğrafı

Dmitry Grinberg4 ay önce

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 profil fotoğrafı

Michael Beckerman4 ay önce

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

SciTech Era profil fotoğrafı

SciTech Era4 ay önce

more detail from GitHub report: for yt video explanation:

SciTech Era profil fotoğrafı

SciTech Era4 ay önce

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

Hotep Jesus profil fotoğrafı

Hotep Jesus4 ay önce

Burn it now before it’s too late!!!

Again? profil fotoğrafı

Again?4 ay önce

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

Edward Smith profil fotoğrafı

Edward Smith4 ay önce

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 profil fotoğrafı

Captain Aaron4 ay önce

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 ☭ profil fotoğrafı

Correcting the Record with Guillotines ☭4 ay önce

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

William Ruider profil fotoğrafı

William Ruider4 ay önce

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 profil fotoğrafı

Rohin Hattiangadi4 ay önce

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

DenisQuaalude profil fotoğrafı

DenisQuaalude4 ay önce

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

A Horse with No Name profil fotoğrafı

A Horse with No Name4 ay önce

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

FocalZero profil fotoğrafı

FocalZero4 ay önce

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

abe kazoo profil fotoğrafı

abe kazoo4 ay önce

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

HeyGen profil fotoğrafı

HeyGen4 ay önce

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

X profil fotoğrafı

X4 ay önce

Nothing new -I played that like 45 years ago.

Rich Tanenbaum profil fotoğrafı

Rich Tanenbaum4 ay önce

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 profil fotoğrafı

arXiv Bangers4 ay önce

Get fucked humans 😂😂 Banger

The Wayne Shaw profil fotoğrafı

The Wayne Shaw4 ay önce

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

Yejun Jang profil fotoğrafı

Yejun Jang4 ay önce

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

UBC Engineering Physics profil fotoğrafı

UBC Engineering Physics4 ay önce

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 profil fotoğrafı

PB4 ay önce

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

Brent P profil fotoğrafı

Brent P4 ay önce

I would fucken smoke that robot in air hockey

Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍 profil fotoğrafı

Dan 𝙏𝙖𝙭𝙖𝙩𝙞𝙤𝙣 𝙄𝙨 𝙏𝙝𝙚𝙛𝙩 Behrman Esq🍍4 ay önce

Now make 2 play each other

Nameless investor #V3403 profil fotoğrafı

Nameless investor #V34034 ay önce

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

Peter Durfee profil fotoğrafı

Peter Durfee4 ay önce

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 profil fotoğrafı

Joshua Hartley4 ay önce

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 profil fotoğrafı

Windy4 ay önce

I think you'd call that machine learning not AI

Thomas Cave profil fotoğrafı

Thomas Cave4 ay önce

20 milliseconds: Let a cat fight against him.

Chris Ferry profil fotoğrafı

Chris Ferry4 ay önce

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 profil fotoğrafı

らく4 ay önce

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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MTS

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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.

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.

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Watch this robot dog learn to walk from scratch in real time! Our new method, APRL, dynamically adjusts exploration constraints to enable fast and performant RL directly in the real world. APRL can also adapt to changes in the terrain. No simulation, no demos. A thread 👇

Watch this robot dog learn to walk from scratch in real time! Our new method, APRL, dynamically adjusts exploration constraints to enable fast and performant RL directly in the real world. APRL can also adapt to changes in the terrain. No simulation, no demos. A thread 👇

Sergey Levine

105,579 görüntüleme • 3 yıl önce

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!

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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One more thing 🚀 Qwen’s agentic capability is no longer limited to the digital world — we’re bringing it into physical world. With our in-house robotic agentic system and navigation model, Qwen can now control a robot to execute tasks in real-world. #qwen #embodied #robotics

One more thing 🚀 Qwen’s agentic capability is no longer limited to the digital world — we’re bringing it into physical world. With our in-house robotic agentic system and navigation model, Qwen can now control a robot to execute tasks in real-world. #qwen #embodied #robotics

barry (xiong-hui) chen

25,192 görüntüleme • 5 ay önce

Just a couple of days ago, and now this??? 98.2% success in simulation?? But most impressively... 10 different tasks in a row! receiving that reward only once! after all 10 are done! First they showed that a smarter way of sampling experience lets robots learn precise assembly and hard locomotion purely in simulation, with zero real-world data and no demonstrations. Now the same team just released AlphaNIST, where one single policy trained from scratch with almost nothing solves all 20 NIST assembly tasks at once. Did I mention 98.2% success in simulation...? Sequences of ten different parts in a row, rewarded only at the very end. And the robot starts inventing its own techniques, different nut-turning motions in tight gaps, reorienting parts against the environment, deciding the order of work itself, none of which was programmed in. It feels like the AlphaGo moment, but for contact-rich assembly: simple objective, massive scale, and strategies that surprise even the people who trained it. Again, thanks to Octi Zhang (Octi Zhang) and the team! 📌 Website: Paper: Follow along if you are interested in robotics and AI! —— Weekly robotics and AI insights. Subscribe free:

Just a couple of days ago, and now this??? 98.2% success in simulation?? But most impressively... 10 different tasks in a row! receiving that reward only once! after all 10 are done! First they showed that a smarter way of sampling experience lets robots learn precise assembly and hard locomotion purely in simulation, with zero real-world data and no demonstrations. Now the same team just released AlphaNIST, where one single policy trained from scratch with almost nothing solves all 20 NIST assembly tasks at once. Did I mention 98.2% success in simulation...? Sequences of ten different parts in a row, rewarded only at the very end. And the robot starts inventing its own techniques, different nut-turning motions in tight gaps, reorienting parts against the environment, deciding the order of work itself, none of which was programmed in. It feels like the AlphaGo moment, but for contact-rich assembly: simple objective, massive scale, and strategies that surprise even the people who trained it. Again, thanks to Octi Zhang (Octi Zhang) and the team! 📌 Website: Paper: Follow along if you are interested in robotics and AI! —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

22,252 görüntüleme • 9 gün önce

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

The TWIML AI Podcast

22,592 görüntüleme • 9 ay önce