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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... show more

AK

524,929 subscribers

293,474 次观看 • 3 年前 •via X (Twitter)

教育科学技术

Anya Rossi• Live Now

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30 条评论

Pablo Moncada-Larrotiz 的头像

Pablo Moncada-Larrotiz3 年前

I hope to see the best human team vs best robot team play in my lifetime. Like Deep Blue vs Kasparov but for football

kache 的头像

kache3 年前

super cool how they penalized it on torque/strain on its joints and got a smoother gait in the policy as a result

AgiDoomerAnon 的头像

AgiDoomerAnon3 年前

You are here.

murtaza 的头像

murtaza3 年前

I saw something similar but couldn't find it where , now i got it 👇

sam laki 的头像

sam laki3 年前

@yacineMTB Bruh how are they playing better than me

Julia (Blackhole) 的头像

Julia (Blackhole)3 年前

In terms of real-world dynamic humanoid locomotion this is nearly the best I've ever seen second only to Atlas, and this is on cheap stock hardware and with a fully automatically trained policy? Amazing work

Alberto Hojel 的头像

Alberto Hojel3 年前

I never knew something could be so adorable yet mind-blowing. Seems like RL is paying off.

Fabian Paischer 的头像

Fabian Paischer3 年前

What does "safe" refer to in this regard? The robots are consistently falling over Also, the only thing that appears to be dynamic in this environment while watching the video is the ball Are there any other dynamic aspects to it?

pranav 的头像

pranav3 年前

DeepMind are based out of London. Calling it soccer is a punishable offence!

My Art Ontario 🐢 的头像

My Art Ontario 🐢3 年前

I want a robo baby yoda

SOPLECHNER SOPLECHNER 的头像

SOPLECHNER SOPLECHNER3 年前

Juegan mejor que la seleccion de Chile jajaja monos qls

7racker 的头像

7racker3 年前

Super toys are coming ☾

Sebastian Bitsch 的头像

Sebastian Bitsch3 年前

Fun watch, are they “stock” OP3 models?

Benny Johansson 的头像

Benny Johansson3 年前

Amazing and fun to watch 😀👍

Jinani 的头像

Jinani3 年前

FIFA 2030 in the making!

Jorge Villegas 的头像

Jorge Villegas3 年前

RIP Messi

Alexander Septian Arfe 的头像

Alexander Septian Arfe3 年前

@SaveToNotion #tweet #ai #ResearchPaper

Astro 的头像

Astro3 年前

It's football

lithium534 的头像

lithium5343 年前

Very impressie.

Amit Khanna 的头像

Amit Khanna3 年前

That is one cruel lady, tripping that poor little robot like that.

Heather Brown 的头像

Heather Brown3 年前

I saw this on 60 minutes last week...too cool!

AU ⏸️⏹️▶️⏩️⏮️⏬️⏯️🔀🔁 的头像

AU ⏸️⏹️▶️⏩️⏮️⏬️⏯️🔀🔁3 年前

@DotCSV

Darryl Mason 的头像

Darryl Mason3 年前

'I'm not playing on the same team as a fkng robot!' 'But that's who the crowds are coming to see! If we don't robot up the team, we lose supporters. The league wants this.' 'But they don't have the skills of human players!' 'They do know how to dive.' 'Yeah....that is true....'

🌞FORMATS 的头像

🌞FORMATS3 年前

@Wickedlander

Nico Bohlinger 的头像

Nico Bohlinger3 年前

When do we see DeepMind in RoboCup?👀

Furkan Gözükara 的头像

Furkan Gözükara3 年前

Nice

Long | 科技与商业沉思录的头像

Long | 科技与商业沉思录3 年前

@SaveToNotion #tweet #AIofPaper

Trist 的头像

Trist3 年前

awww adorable!

BabbleBones 的头像

BabbleBones3 年前

@yacineMTB This sparks joy

Rom_AI 的头像

Rom_AI3 年前

In the future, we will have World vs Robots in Wembley.

相关视频

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

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

Interesting Engineering

75,959 次观看 • 3 年前

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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372,039 次观看 • 3 年前

Soccer players have to master a range of dynamic skills, from turning and kicking to chasing a ball. How could robots do the same? ⚽ We trained our AI agents to demonstrate a range of agile behaviors using reinforcement learning. Here’s how. 🧵

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448,145 次观看 • 2 年前

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46,795 次观看 • 1 年前

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