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🤖What if a robot could perform a new task just from a natural language command, with zero demonstrations? Our new work, NovaFlow, makes it possible! We use pre-trained video generative model to create a video of the task, then translate it into a plan for real-world robot execution. 1/6... show more

Hongyu Li

1,668 subscribers

105,915 次观看 • 11 个月前 •via X (Twitter)

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34 条评论

Hongyu Li 的头像

Hongyu Li11 个月前

We start by giving a video model a text prompt like "hang the mug." It generates a short clip of a human performing the task, acting as a visual instruction manual. This video provides the commonsense physical knowledge needed for the robot to understand the motion. 🎥 2/6

Hongyu Li 的头像

Hongyu Li11 个月前

The generated video is 2D, so how does the robot understand 3D space? We lift it! 📏 A monocular video depth estimation model predicts depth for every frame. Then, we calibrate it with the robot's real-world depth camera for precise spatial awareness. 3/6

Hongyu Li 的头像

Hongyu Li11 个月前

🤔 But how do you bridge the gap between that video and physical control? We introduce "actionable 3D object flow," a novel representation distilled from the video that guides the robot’s motion for complex rigid, articulated, and even deformable objects. 🌟 4/6

Hongyu Li 的头像

Hongyu Li11 个月前

NovaFlow shows incredible generalization. From rigid blocks to deformable ropes, on both a tabletop arm and a mobile Spot! It adapts to new objects and embodiments without any retraining. It achieves SOTA zero-shot results, even outperforming policies trained on 10-30 demos. 🏆 5/6

Hongyu Li 的头像

Hongyu Li11 个月前

By leveraging the commonsense knowledge in video models, NovaFlow helps solve the robot data bottleneck. This is a leap towards more general-purpose robots. 🚀 Dive deeper into our work: 📜 Paper: 🌐 Project Website: 👀 P.S. You can check out the 3D generated videos and planned robot trajectories in a live viewer on our website! 6/6

Hongyu Li 的头像

Hongyu Li11 个月前

This is a collaboration with @ling_feng_sun, @YafeiHuCMU, Duy Ta, Jenny Barry, and @jiahuifu_carol.

Nils 的头像

Nils11 个月前

Beautiful.

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks, Nils!

Xiatao Sun 的头像

Xiatao Sun11 个月前

Using video generation models to create task plans from language is clever! The actionable 3D object flow representation seems like the key to making this work across embodiments.

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks, Xiatao. I believe video models capture the most general form of dynamics, although they can’t be directly understood by robots. Actionable flow enables robots to comprehend the generated videos.

Zhiyang (Frank) Dou 的头像

Zhiyang (Frank) Dou11 个月前

Nice work!

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks, Zhiyang!

Bozheng Li 的头像

Bozheng Li11 个月前

great work!

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks, Bozheng!

Balveer Singh 的头像

Balveer Singh11 个月前

Wow, this is such a cool approach.

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks🙏

Ted Xiao 的头像

Ted Xiao11 个月前

Nice work!

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks, Ted! 🙏

Sam Green 的头像

Sam Green11 个月前

Laying the groundwork for Soma

Atharva Sawant 的头像

Atharva Sawant11 个月前

So cool

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks

Cryptlesh 的头像

Cryptlesh11 个月前

Wow. It’s like imagining whole take on head and then implementing it like human break does. Kind of.

Hongyu Li 的头像

Hongyu Li11 个月前

Exactly, a video model serves as the imagination capabilities of human beings.

Bercan 的头像

Bercan11 个月前

Great work. Can you try on these examples in mcap.

Hongyu Li 的头像

Hongyu Li11 个月前

Thanks for the great suggestions! Egocentric data is more challenging, and we’re still working on it!

Bercan 的头像

Bercan11 个月前

Happy to work together we are also working on real2sim

Stanley Wei 的头像

Stanley Wei11 个月前

That will save a lot of time. Hope you post some cool demos.

BlockChan 的头像

BlockChan9 个月前

holy shit i personaly owned name for a while and have no idea what to build

towerofshadow 的头像

towerofshadow11 个月前

is it more effective to train robots using human actions that are tracked using actionable flow, or to teleoperate robots into direct movement data?

Hongyu Li 的头像

Hongyu Li11 个月前

NovaFlow is object-centric and embodiment-agnostic. So it should work the same for both human and robot data. In practice, we find video generative models work better with generating human data.

Danny Ng 的头像

Danny Ng11 个月前

I wonder how long it takes to perform one task?

Hongyu Li 的头像

Hongyu Li11 个月前

It depends on the choice of video model. For Veo, it takes around 2-3min for the entire pipeline.

Urs Gehrig 的头像

Urs Gehrig11 个月前

Zero shot Robotic AI manipulation.

Mariano Phielipp 的头像

Mariano Phielipp6 个月前

Interesting work. How you see the work of Florence, Manuelli, Redrake in the context of this work Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation Florence,Manuelli, Russ Tedrake?

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S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment.

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World Labs co-founder Justin Johnson predicts a real-to-sim-to-real future where Atlas could train a robot for any new environment in minutes using just 5 photos from your phone: "One is this notion of fully in-context learning. Maybe I've got a robotics foundation model, then I can demonstrate the robot once how a task should be performed, and that's enough for the robot to figure out." "Another version is real to sim to real. Maybe I've got my pre-trained robotics foundation model, but I want to adapt it to this particular environment, like this studio, this table, moving this microphone around." "There's a version where you could come in here and take just a couple casual videos with your phone or a couple images with your phone, use Atlas to reconstruct the space, and now stage all kinds of robotics interactions in this studio space." "If you could lower the barrier to doing that, you could do it in five minutes to take five photos, upload it to Atlas, have it generate a simulation, maybe describe in natural language what kind of task you want the robot to do, have an agent go build that simulation for you, RL fine-tune your general purpose robotics foundation model, and now you've got a robot maybe in the span of a couple minutes that could come in and is perfectly adapted to this space." World Labs

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22,513 次观看 • 2 个月前

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18,901 次观看 • 19 天前

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47,566 次观看 • 7 个月前

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Introducing FLUX-mimic, a next-generation Video-Action Model for general purpose dexterity, developed in partnership with Black Forest Labs. Late last year we published mimic-video and introduced Video-Action Models (VAM): a new family of robotics foundation models built on top of video generation models. We showed that robot control reduces to visual prediction, and that robot capability is downstream of improvements in video modeling accuracy. The obvious implication was that advances in the video modeling frontier would directly translate to increased capabilities in end-to-end robot learning. FLUX-mimic is that thesis at frontier scale: We've applied our VAM architecture to the strongest video backbone available today, FLUX 3 from Black Forest Labs, and trained it on data from our own robots and wearables. General-purpose dexterity, running on a single GPU on premises. Because the model already understands world dynamics, it needs far fewer demonstrations to learn a new task. This is game-changing for our mission to deploy robots to factory floors, where industrial robot data is scarce and expensive to collect. We're now testing and deploying FLUX-mimic with manufacturing leaders like Audi USA, on complex, multi-step manipulation long considered impossible for conventional automation.

mimic

118,172 次观看 • 1 个月前

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Excited to release τ0-WM: an open-source unified video-action world model for robotic manipulation. It's a 5B-parameter robotic foundation model trained on 27.3K hours of real-robot teleoperation, UMI-style demonstrations, and egocentric interaction videos.

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In my first week at Generalist, I trained a robot to pour liquids using GEN-1 🤖💧 I wanted to challenge the robot with a non-rigid manipulation task, so liquid felt like the perfect choice. The task involved: - unscrewing the bottle cap - pouring liquid into espresso glasses - rebalancing uneven pours Best of all, the robot was able to complete the task fully autonomously 3 times in a row (out of 3)! Pour-fect 😉 Excited for the journey ahead and grateful to be building alongside such an incredible team!

Kylie Ying

40,386 次观看 • 3 个月前

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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,090 次观看 • 8 个月前

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

20,968 次观看 • 1 年前