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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 görüntüleme • 11 ay önce •via X (Twitter)

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Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

🤔 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 profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

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

Nils profil fotoğrafı

Nils11 ay önce

Beautiful.

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks, Nils!

Xiatao Sun profil fotoğrafı

Xiatao Sun11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Zhiyang (Frank) Dou11 ay önce

Nice work!

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks, Zhiyang!

Bozheng Li profil fotoğrafı

Bozheng Li11 ay önce

great work!

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks, Bozheng!

Balveer Singh profil fotoğrafı

Balveer Singh11 ay önce

Wow, this is such a cool approach.

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks🙏

Ted Xiao profil fotoğrafı

Ted Xiao11 ay önce

Nice work!

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks, Ted! 🙏

Sam Green profil fotoğrafı

Sam Green11 ay önce

Laying the groundwork for Soma

Atharva Sawant profil fotoğrafı

Atharva Sawant11 ay önce

So cool

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

Thanks

Cryptlesh profil fotoğrafı

Cryptlesh11 ay önce

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

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

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

Bercan profil fotoğrafı

Bercan11 ay önce

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

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

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

Bercan profil fotoğrafı

Bercan11 ay önce

Happy to work together we are also working on real2sim

Stanley Wei profil fotoğrafı

Stanley Wei11 ay önce

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

BlockChan profil fotoğrafı

BlockChan9 ay önce

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

towerofshadow profil fotoğrafı

towerofshadow11 ay önce

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 profil fotoğrafı

Hongyu Li11 ay önce

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 profil fotoğrafı

Danny Ng11 ay önce

I wonder how long it takes to perform one task?

Hongyu Li profil fotoğrafı

Hongyu Li11 ay önce

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

Urs Gehrig profil fotoğrafı

Urs Gehrig11 ay önce

Zero shot Robotic AI manipulation.

Mariano Phielipp profil fotoğrafı

Mariano Phielipp6 ay önce

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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Humanoid robotics is entering a new phase： Xiaomi Robotics 0 (XR-0) explores learning manipulation directly from large-scale human videos — aligning video understanding with robot embodiment to generate executable actions. Watch humans->learn representations ->map to robot control. Robot data is scarce. Internet video is not. If video pretraining transfers reliably into embodied control, the scaling law of robotics changes. Deployment speed becomes a function of model alignment, not just hardware iteration. We’re moving from engineered skills to learned behavior. That’s a structural shift. project:

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22,513 görüntüleme • 2 ay önce

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Show it a single human demonstration of a long-horizon task and the robot performs it right away. No fine-tuning, no retraining. Skild has released S1, a robot foundation model where you specify the task with a video demonstration instead of a language instruction. Why video, not language Language works for "hand me the mug." It falls apart for anything delicate or long-horizon. You don't learn to fold a fitted sheet from a sentence. So S1 takes a video demo into its context window and translates it to its own body and scene. What it does - Tasks up to 10 minutes, dozens of sequential steps - Unseen tasks: potting plants, pour-over coffee, kit assembly, pancakes. Skild says even the underlying primitives, like flipping a pancake, weren't in pre-training - Recovers from its own mistakes without being trained to - Improves on flawed demos. Demonstrator drops an egg, S1 doesn't make that mistake. It reads the demo as a goal, not a trajectory to copy.

The Humanoid Hub

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47,566 görüntüleme • 7 ay önce

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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

118,128 görüntüleme • 1 ay önce

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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.

Jianlan Luo

63,831 görüntüleme • 3 ay önce

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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 görüntüleme • 3 ay önce

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 →

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 görüntüleme • 8 ay önce

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 görüntüleme • 1 yıl önce