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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 Aufrufe • vor 11 Monaten •via X (Twitter)

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34 Kommentare

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

🤔 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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Nils

Nilsvor 11 Monaten

Beautiful.

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks, Nils!

Profilbild von Xiatao Sun

Xiatao Sunvor 11 Monaten

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.

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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.

Profilbild von Zhiyang (Frank) Dou

Zhiyang (Frank) Douvor 11 Monaten

Nice work!

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks, Zhiyang!

Profilbild von Bozheng Li

Bozheng Livor 11 Monaten

great work!

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks, Bozheng!

Profilbild von Balveer Singh

Balveer Singhvor 11 Monaten

Wow, this is such a cool approach.

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks🙏

Profilbild von Ted Xiao

Ted Xiaovor 11 Monaten

Nice work!

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks, Ted! 🙏

Profilbild von Sam Green

Sam Greenvor 11 Monaten

Laying the groundwork for Soma

Profilbild von Atharva Sawant

Atharva Sawantvor 11 Monaten

So cool

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

Thanks

Profilbild von Cryptlesh

Cryptleshvor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Bercan

Bercanvor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Bercan

Bercanvor 11 Monaten

Happy to work together we are also working on real2sim

Profilbild von Stanley Wei

Stanley Weivor 11 Monaten

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

Profilbild von BlockChan

BlockChanvor 9 Monaten

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

Profilbild von towerofshadow

towerofshadowvor 11 Monaten

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

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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.

Profilbild von Danny Ng

Danny Ngvor 11 Monaten

I wonder how long it takes to perform one task?

Profilbild von Hongyu Li

Hongyu Livor 11 Monaten

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

Profilbild von Urs Gehrig

Urs Gehrigvor 11 Monaten

Zero shot Robotic AI manipulation.

Profilbild von Mariano Phielipp

Mariano Phielippvor 6 Monaten

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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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 →

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