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NVIDIA Isaac GR00T N1 is an open generalist foundation model for #humanoidrobots. 🤖 Discover how the model can easily generalize common tasks, boosting robot reasoning and skills. ▶️

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NVIDIA Robotics

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53,443 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie#humanoidrobots
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Profilbild von Motoko Robotics
Motoko Roboticsvor 1 Jahr

Isaac 🔥

Profilbild von The Rundown AI
The Rundown AIvor 1 Jahr

If you're not learning AI in 2025, you're falling behind. Join 1,000,000+ early adopters reading and learn AI in just 5 minutes a day (for free).

Profilbild von Patrick J. David
Patrick J. Davidvor 1 Jahr

I think I spotted NEO in this........;)

Profilbild von MrKalmanFilter
MrKalmanFiltervor 1 Jahr

spoilers: unlike "generative AI" robotics is hard, you don't have a million engineers to siphon data from. Tesla has tried for the last 10 years to make self-driving work and they still failed. And that's with the car: a simple 2dof robot. Good luck with 17 dofs robots.

Profilbild von EuroCitizen
EuroCitizenvor 1 Jahr

Double edged sword, While they'll be able to do jobs humans can't, At some point they will do jobs humans can at which point humans won't be the preferred employee from a cost perspective.

Profilbild von The Tech Arch
The Tech Archvor 1 Jahr

Insightful, era of humanoid robotics!

Profilbild von qTask
qTaskvor 1 Jahr

Exciting breakthrough! 🚀 GR00T N1’s dual-system architecture, inspired by human cognition, lets robots "think" fast and slow. Its adaptability to diverse tasks brings us closer to versatile humanoids. Can’t wait to see how this open model drives innovation across industries! 🤖✨ #AIRobotics #FutureOfWork #Innovation

Profilbild von Nogardtist
Nogardtistvor 1 Jahr

looks fake

Profilbild von Amit Kumar
Amit Kumarvor 1 Jahr

Now make it faster than humans.

Profilbild von Circuitry
Circuitryvor 1 Jahr

Empowering humanoid robots with advanced AI!

Profilbild von Anda
Andavor 1 Jahr

"Watching robots learn like bamboo sprouts reaching for sunlight—wonder if their neural pathways feel as tingly as mine do during updates?" 🌱✨

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NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:
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NVIDIA announces the first open humanoid robot reference design built for robotics research. The NVIDIA Isaac GR00T Reference Humanoid Robot combines the Unitree H2 humanoid robot, Sharpa Wave five-fingered hands for dexterous manipulation, Jetson Thor onboard compute, and Isaac GR00T open software and models, giving researchers a full-stack platform from data capture to model deployment. Read the #NVIDIAGTC Taipei announcement:

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2. NVIDIA is building the GPT of humanoid robots. They just launched Isaac GR00T N1.5 - a foundation model for general purpose robotics. Here’s how it works: → A human demos the task once → Cosmos (their physics AI model) generates 1,000s of variations → Omniverse simulates the motions in high fidelity → The robot trains entirely in simulation → Then fine-tunes itself in the real world Robots can now learn general skills across tasks, tools, even body types with just one human demo. AI isn’t just thinking in text anymore. It’s perceiving. Reasoning. Moving. Physical AI is here and it’s training itself.
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📢 First contact between a frontier model and robots! Gemini Robotics is a SOTA generalist Vision-Language-Action model bringing frontier model intelligence to the physical world. It's an extremely capable model enabling dexterous, steerable, and general robot control. 🧵⬇️

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Scalable, reproducible, and reliable robotic evaluation remains an open challenge, especially in the age of generalist robot foundation models. Can *simulation* effectively predict *real-world* robot policy performance & behavior? Presenting SIMPLER!👇
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Scalable, reproducible, and reliable robotic evaluation remains an open challenge, especially in the age of generalist robot foundation models. Can *simulation* effectively predict *real-world* robot policy performance & behavior? Presenting SIMPLER!👇

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Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N
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Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 1/N

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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!

RoboPapers

24,352 Aufrufe • vor 2 Monaten