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

MachinePix

207,009 subscribers

16,986,259 Aufrufe • vor 2 Jahren •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

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10 Kommentare

Profilbild von 𝕏 Posting

𝕏 Postingvor 2 Jahren

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

Profilbild von Bharat Vasan 🇺🇸

Bharat Vasan 🇺🇸vor 2 Jahren

if skynet takes over, videos like this will be why

Profilbild von MRG🥍🏀🏈🏉🤿👨‍🌾

MRG🥍🏀🏈🏉🤿👨‍🌾vor 2 Jahren

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

Profilbild von Joe Ward ▲

Joe Ward ▲vor 2 Jahren

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

Profilbild von { Luh }

{ Luh }vor 2 Jahren

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

Profilbild von Paul Leavitt

Paul Leavittvor 2 Jahren

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

Profilbild von Tom Bielecki

Tom Bieleckivor 2 Jahren

I made one of those! —he’s 2

Profilbild von @stevenvoiceover

@stevenvoiceovervor 2 Jahren

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

Profilbild von trøut

trøutvor 2 Jahren

soon

Profilbild von G.I Joe

G.I Joevor 2 Jahren

Imagine a few of these running around cleaning the house

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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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