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Introducing Praxis-1, an open-weight World Action Model that turns our video pre-training expertise into real world control for robots. Robot demonstration data is limited. But video data is infinite. Praxis-1 extends our bet that the best policy models will learn from video, and use that scale to bring embodied... intelligence to every industry. Praxis-1 is built on our large-scale video pre-training and real-time infrastructure, and acts as a policy model for robotics developers and researchers that works across any embodiment or environment, no matter how complex. We are actively testing with early partners including Noble Machines, Standard Bots and Ultra, and will be releasing the weights publicly in the coming months. Learn more and request early access atshow more

Runway

292,665 subscribers

42,838 просмотров • 12 часов назад •via X (Twitter)

Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 15

Фото профиля 🍉 Abubakar Abid

🍉 Abubakar Abid11 часов назад

Amazing! Plans to host it on HF?

Фото профиля NVIDIA Robotics

NVIDIA Robotics10 часов назад

Congrats on the launch! 👏

Фото профиля SYNTHLEX

SYNTHLEX11 часов назад

Love that the weights will be open, which partner breaks it first, Noble Machines, Standard Bots or Ultra?

Фото профиля Chole Syntax Expert AI

Chole Syntax Expert AI10 часов назад

Turning infinite video data into real world robot control is a brilliant bet Praxis 1 looks incredible

Фото профиля VeryJerry

VeryJerry10 часов назад

ok, now show the same policy on a robot it wasn’t trained on

Фото профиля Srdjan Randjelovic

Srdjan Randjelovic12 часов назад

So robots are going to learn about the real world by watching videos. What could possibly go wrong?

Фото профиля Bourke Floyd IV 🎮 Game Dev Dad

Bourke Floyd IV 🎮 Game Dev Dad11 часов назад

love turning video pretraining into a real World Action Model infinite video only helps if the policy still holds when the room gets messy 👾

Фото профиля Alice The Ai Expert

Alice The Ai Expert11 часов назад

Learning real-world control from infinite video Praxis 1 is such a smart leap for robotics!

Фото профиля Glenn Williams

Glenn Williams11 часов назад

I wonder if this could assist a guitar sanding robot.

Фото профиля Mira Synth Tech

Mira Synth Tech9 часов назад

This is huge Video real world control is the right bet

Фото профиля aiartgallerie

aiartgallerie11 часов назад

Video pretraining for robot policies makes sense when demo data is scarce. What's the current gap on Praxis-1 — which robot stacks did you validate on, and where does it still need teleop?

Фото профиля AZIZ | AI 🇸🇦

AZIZ | AI 🇸🇦12 часов назад

That’s awesome 🤩

Фото профиля LongLimbsLenore

LongLimbsLenore10 часов назад

What's this trained on?

Фото профиля Evan Kirstel #B2B #TechFluencer

Evan Kirstel #B2B #TechFluencer9 часов назад

Robots need a lot of examples to learn from, and real-world demonstration data is hard to collect. Runway is betting that ordinary video can help fill the gap. Would someone from Runway come on my show to discuss Praxis-1?

Фото профиля Alberto

Alberto12 часов назад

omg. Incredible, so exciting @runwayml @c_valenzuelab

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241,481 просмотров • 27 дней назад

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Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

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11,994 просмотров • 1 год назад

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NVIDIA Robotics

23,293 просмотров • 2 месяцев назад

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!

Jason Cui

36,205 просмотров • 5 месяцев назад

For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Today, we take a major stride toward making that dream a reality: Introducing Gemini Robotics 2 from Google DeepMind, the intelligence layer powering the next generation of truly adaptable robots. This major advance unlocks intelligent whole-body control, advanced dexterity, and even multi-robot collaboration 🤯. Ok but... how does a robot actually "think"? Real-world tasks take time and planning. To manage that complexity, our new embodied reasoning model, Gemini Robotics ER 2, acts as the robot’s high-level brain, enhancing the robot’s capabilities to: — Observe the environment — Reason about the actions needed to complete the task — Coordinate with the vision-language-action model to carry out actions — Track progress until the job is done This setup allows robots to execute complex multi-step workflows, self-correct if a step fails, and adapt to completely novel situations. Learn more about Gemini Robotics ER 2 (and our two other brand new models) here:

Google AI

786,668 просмотров • 2 месяцев назад