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DeepMind training bipedal robots to play soccer using deep reinforcement learning.

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MachinePix

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16,986,259 görüntüleme • 2 yıl önce •via X (Twitter)

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𝕏 Posting profil fotoğrafı
𝕏 Posting2 yıl önce

it'll remember this day 😂 "push me around one more time"

Bharat Vasan 🇺🇸 profil fotoğrafı
Bharat Vasan 🇺🇸2 yıl önce

if skynet takes over, videos like this will be why

MRG🥍🏀🏈🏉🤿👨‍🌾 profil fotoğrafı
MRG🥍🏀🏈🏉🤿👨‍🌾2 yıl önce

Sure, but when I use "deep reinforcement learning" on my kid they call Child Services.

Joe Ward ▲ profil fotoğrafı
Joe Ward ▲2 yıl önce

What happens when the AI realizes that finger breaking might be beneficial to producing positive outcomes in soccer play, under some conditions?

{ Luh } profil fotoğrafı
{ Luh }2 yıl önce

Why she keep knocking poor buddy over like that He’s trying his best!!

Paul Leavitt profil fotoğrafı
Paul Leavitt2 yıl önce

Please be nice to the baby robot, he’s gonna turn on us someday. 🥺

Tom Bielecki profil fotoğrafı
Tom Bielecki2 yıl önce

I made one of those! —he’s 2

@stevenvoiceover profil fotoğrafı
@stevenvoiceover2 yıl önce

Is it wrong that I’m feeling sorry for this robot? 🙄😂

trøut profil fotoğrafı
trøut2 yıl önce

soon

G.I Joe profil fotoğrafı
G.I Joe2 yıl önce

Imagine a few of these running around cleaning the house

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Miniature humanoid robots master a simplified soccer game using Deep Reinforcement Learning, showcasing surprising agility, quick fall recovery, and strategic understanding of the game. #engineering
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Miniature humanoid robots master a simplified soccer game using Deep Reinforcement Learning, showcasing surprising agility, quick fall recovery, and strategic understanding of the game. #engineering

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Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning investigated the application of Deep Reinforcement Learning (Deep RL) for low-cost, miniature humanoid hardware in a dynamic environment, showing the method can synthesize sophisticated and safe movement skills making up complex behavioral strategies in a simplified one-versus-one (1v1) soccer game abs: project page:
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Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning investigated the application of Deep Reinforcement Learning (Deep RL) for low-cost, miniature humanoid hardware in a dynamic environment, showing the method can synthesize sophisticated and safe movement skills making up complex behavioral strategies in a simplified one-versus-one (1v1) soccer game abs: project page:

AK

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Google's DeepMind trained robots how to play soccer. Here's what I found:
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Google's DeepMind trained robots how to play soccer. Here's what I found:

Alex Banks

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Training AI to Play Pokemon with Reinforcement Learning by Peter Whidden github: youtube:
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Training AI to Play Pokemon with Reinforcement Learning by Peter Whidden github: youtube:

AK

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NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:
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NEWS: DeepMind teaches robots to play soccer. In a paper released today Google's DeepMind details how they used Deep Reinforcement Learning (Deep RL) to train low-cost, miniature humanoid robots in dynamic environments, allowing them to play a simplified one-versus-one (1v1) soccer game. The robots, equipped with 20 actuated joints, were initially trained in simulation using the MuJoCo physics engine. Through this training, they learned robust and dynamic movement skills like rapid fall recovery, walking, turning, and kicking. The robots seamlessly transitioned between these skills, even surpassing expectations, and developed a basic strategic understanding of the game. During matches, the trained robots demonstrated agile skills such as turning, kicking moving balls, and dynamic defensive blocking. They quickly combined these skills, showcasing their adaptability and outperforming scripted baselines. The robots walked faster, got up quicker, and kicked faster than their counterparts. Individual skills were first trained in isolation within the simulation environment, and then composed in a self-play setting. The robots successfully transferred these skills to real-world scenarios. Full paper can be found here:

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Watch Gemini 2.5 Pro implement a landmark Google DeepMind research paper. 🕹️ It codes the reinforcement learning algorithm, visualizes the training live and even debugs errors. ↓
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Watch Gemini 2.5 Pro implement a landmark Google DeepMind research paper. 🕹️ It codes the reinforcement learning algorithm, visualizes the training live and even debugs errors. ↓

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Figure 02 robots are now walking with a smoother gait and increased speed, thanks to sim-to-real reinforcement learning.
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Our CoRL 2024 paper shows Reinforcement Learning can allow robots to learn skills via real-world practice, without any demonstrations or simulation engineering. Rewards are provided using language/vision models, and mobility of robots enables autonomous exploration. 1/N
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Our CoRL 2024 paper shows Reinforcement Learning can allow robots to learn skills via real-world practice, without any demonstrations or simulation engineering. Rewards are provided using language/vision models, and mobility of robots enables autonomous exploration. 1/N

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Its nice to see the recent excitement around Q-learning! Here's a clip from a video all about Deep Reinforcement Learning, including Deep Q Learning #machinelearning #ai
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Its nice to see the recent excitement around Q-learning! Here's a clip from a video all about Deep Reinforcement Learning, including Deep Q Learning #machinelearning #ai

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LimX Dynamics P1 is a versatile bipedal robot equipped with reinforcement learning capabilities, enabling it to master challenging terrains such as forests and ditches while maintaining its balance.
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TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.
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