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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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Interesting Engineering

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75,943 görüntüleme • 3 yıl önce •via X (Twitter)

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drlewtz profil fotoğrafı
drlewtz3 yıl önce

The machines will remember this...

Soccer Bot™ profil fotoğrafı
Soccer Bot™1 yıl önce

Experience the bot completely reimagined. 🌟 A completely new design, reminders for match prediction, notifications when games are interrupted, continued or postponed. New commands like /features and /standings and new languages and much more.

Russell Irion profil fotoğrafı
Russell Irion3 yıl önce

Already flopping?!

alberto benarroch profil fotoğrafı
alberto benarroch3 yıl önce

Amazing

My name is M. Only M profil fotoğrafı
My name is M. Only M3 yıl önce

Con empujarlo una vez alcanzaba, cuando crezca, sea semáforo, la reconozca y no le de luz verde que no se queje.. Y mejor que no forme su propio partido y gane las elecciones xq..

Wagner Paiva Fernandes (Tesla is CHOAM) profil fotoğrafı
Wagner Paiva Fernandes (Tesla is CHOAM)3 yıl önce

@BenShead1 So cruel.

Tempered profil fotoğrafı
Tempered3 yıl önce

Computers never forget. On day that lil mufker is going to upload itself into a drone and kill every tech who ever pushed it around.

N.Delgado profil fotoğrafı
N.Delgado3 yıl önce

Hate that b.t.c.h 😡

Huanchi X profil fotoğrafı
Huanchi X3 yıl önce

Estamos a un paso de los T800 😱😱😱

AngelTyrael profil fotoğrafı
AngelTyrael3 yıl önce

can you stop pushing him? first 3 times i get it , it learned to get up but 6 times later it was fucking annoying

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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:

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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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