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Real-world robot data is expensive and slow to collect, creating a major challenge for humanoid development. 🤖 The NVIDIA GR00T N1.6 open vision language action model is pre-trained on a diverse mix of data, including thousands of hours of Stanford Vision and Learning Lab’s BEHAVIOR simulation data, which covers... show more

NVIDIA Robotics

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13,535 views • 8 months ago •via X (Twitter)

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NVIDIA Robotics8 months ago

Developers can download and experiment with a BEHAVIOR-1K–post-trained GR00T N1.6 model to achieve stronger performance on BEHAVIOR benchmark tasks. 👉

Alex8 months ago

@StanfordSVL Would be nice if the Unitree G1 real-world policy deployment code is open sourced as well 😊

dronx78 months ago

"Impressive progress in VLA, but we must ask. Are we just scaling 'Acquisition' or aiming for true 'Mastery'? Current data-heavy approaches are inevitably slow and costly. We need to move beyond brute-force learning towards a Hierarchical Interpretive Architecture. Instead of teaching a robot 1,000 ways to pick up an apple, we should equip it with a 'Constitutional Reasoning Engine' that interprets physical causality in real-time. The future of robotics isn't just about more data—it's about the hierarchy of interpretation."

sid8 months ago

@StanfordSVL the interesting question is how much domain randomization you need to make sim hours actually transfer. otherwise you're just training on fake data that doesn't generalize

Carina Nicolosi8 months ago

@StanfordSVL

Amit8 months ago

@StanfordSVL Simulated data is the scaffolding of modern robotics, but the real test is the transfer gap. GR00T’s scale is impressive, yet the subtle chaos of physical reality always finds holes in even the fattest datasets.

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23,871 views • 3 months ago

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20,486 views • 1 year ago

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22,669 views • 3 months ago

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46,715 views • 1 year ago

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467,710 views • 1 year ago

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20,968 views • 1 year ago

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38,867 views • 4 months ago

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94,149 views • 9 months ago

Happy to share what I’ve been working on since joining Genesis! GENE-26.5 is a one-of-a-kind, robotics-native multimodal foundation model that learns from diverse, in-the-wild data across modalities and outputs actions enabling a 54-DoF robot system to perform the most dexterous, long-horizon manipulation tasks to date—approaching human-level capability. This is the result of innovations across the full stack—data collection and processing, robot systems, model architecture, training strategies, and scalable evaluation infrastructure.

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20,454 views • 5 months ago

Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

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1,815,959 views • 2 months ago

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82,970 views • 1 year ago

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 Robotics

174,660 views • 4 months ago

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes evencheaper with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

Abhishek Gupta

13,358 views • 1 year ago

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

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NVIDIA Robotics

10,840 views • 6 months ago

An interactive world model developed by NVIDIA in collaboration with academic partners. - DreamDojo turns egocentric human video data into physical intelligence. - Human data is more scalable than robotics data but lacks action labels. - To solve this, a dedicated action model extracts latent actions by identifying physics and motion deltas between frames. Training - A massive 44k hours of video data are used for pre-training. - Post-training on small-scale robot datasets maps human physics to specific robot embodiments. - An additional distillation stage converts the model into an autoregressive, few-step diffusion model, enabling real-time, action-controllable simulation. Primary Use Cases - Live Teleoperation: Controlling a robot inside a world simulation in real-time. - Model-based Planning: Previewing and curating the best actions for improved success. - Policy Evaluation: Testing robot policies in realistic, out-of-distribution scenarios. Everything that's open-sourced: weights, code, post-training dataset, eval set, and details to reproduce.

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The Humanoid Hub

11,575 views • 7 months ago

Announcing RoboFuME🤖💨, a system for autonomous & efficient real-world robot learning that 1. pre-trains a VLM reward model and a multi-task RL policy from diverse off-the-shelf demo data; 2. runs RL fine-tuning online with the VLM reward model. 🔗 🧵↓

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Jingyun Yang

88,312 views • 3 years ago

It’s long been a dream of roboticists to be able to teach a robot in simulation so as to skip the long and expensive process of collecting large amounts of real-world training data. However, building simulations for robot tasks is extremely hard. Ideally, we could go from real data to a useful simulation. This is exactly what Guangqi Jiang and his co-authors do. they use 3d Gaussian splatting to reconstructed scenes which let them create interactive environments that, when combined with a physcs engine, allow for training robot policies that show zero-shot sim-to-real transfer (i.e., using no real-world demonstrations). To learn more, watch Episode 56 of Robopapers with Michael Cho - Rbt/Acc and Chris Paxton now!

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RoboPapers

20,434 views • 9 months ago