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Introducing RoboCat, a new AI model designed to operate multiple robots. 🤖 It learns to solve new tasks on different robotic arms with as few as 100 demonstrations - and improves skills from self-generated training data. Find out more:

Google DeepMind

1,458,263 subscribers

410,272 次观看 • 3 年前 •via X (Twitter)

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10 条评论

Google DeepMind 的头像

Google DeepMind3 年前

🟥 Actions like shape matching and building structures are complex for robots. They have to master how to control each joint, coordinate movements, and know the physics of their surroundings. This embodied intelligence comes naturally to humans but it’s very difficult for AI.

Google DeepMind 的头像

Google DeepMind3 年前

🔵 RoboCat learns faster than other state-of-the-art models because it draws from a large, diverse dataset. This feature will advance robotics research as it reduces the need for human-supervised training - a key step to creating general-purpose robots:

Bobcat 的头像

Bobcat3 年前

@DeepMind But what of the cat operating all this

Kye Gomez (ultra/acc) 的头像

Kye Gomez (ultra/acc)3 年前

@DeepMind IS IT OPEN SOURCE?

co-varun 的头像

co-varun3 年前

@DeepMind Whaaaaaaaat! Nooo wayyyyy!

Ben Schulz 的头像

Ben Schulz3 年前

@DeepMind This will be huge for the eventual autonomous construction vehicles.

Viral Vora- Towards Super intelligence 的头像

Viral Vora- Towards Super intelligence3 年前

@DeepMind A tweet that should have been a presentation

Aaron Francis 的头像

Aaron Francis3 年前

@DeepMind @KennethCassel sure you've seen this, but tagging you anyway! Seems like you're in a good space 🔥

Astur81 的头像

Astur813 年前

@DeepMind @DotCSV

heyheymymy 的头像

heyheymymy3 年前

@DeepMind For our generation, when we get older, we may be taken care by robots. Every household will have a robot, just like every household will have a car.

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Today, we announced 𝗥𝗧-𝟮: a first of its kind vision-language-action model to control robots. 🤖 It learns from both web and robotics data and translates this knowledge into generalised instructions. Find out more:

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537,808 次观看 • 2 年前

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Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

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Scientists developed HumanX, a system that lets humanoid robots learn new skills by watching just one short human video. It learns directly from demonstrations instead of large datasets or complex training rewards. Using this, the Unitree G1 humanoid robot learned 10 different real-world skills without extra training.

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We’re bringing powerful AI directly onto robots with Gemini Robotics On-Device. 🤖 It’s our first vision-language-action model to help make robots faster, highly efficient, and adaptable to new tasks and environments - without needing a constant internet connection. 🧵

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21,024 次观看 • 1 个月前

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