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Zero teleoperation. Zero real-world data. ➔ Autonomous humanoid loco-manipulation in reality. Introducing VIRAL: Visual Sim-to-Real at Scale. We achieved 54 autonomous cycles (walk, stand, place, pick, turn) using a simple recipe: 1. RL 2. Simulation 3. GPUs Website: Arxiv: Deep dive with me: 🧵

Tairan He

15,933 subscribers

270,055 次观看 • 11 个月前 •via X (Twitter)

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Tairan He 的头像

Tairan He11 个月前

Data is the bottleneck of robot learning. The community relies heavily on expensive, slow, human-collected data. We flipped the script. VIRAL trains entirely inside NVIDIA Isaac Lab. We accelerate physics by 10,000x real-time with photorealistic rendering. No human data required. 2/

Tairan He 的头像

Tairan He11 个月前

The Pipeline: Teacher: We train a privileged teacher with full state access in sim. Student: We distill a vision-based student policy from the teacher. This uses large-scale simulation with tiled rendering, combining online DAgger and Behavior Cloning across tens of GPUs. 3/

Tairan He 的头像

Tairan He11 个月前

Scale is not optional. We scaled visual sim-to-real compute up to 64 GPUs. We discovered that for long-horizon tasks (like loco-manipulation), large-scale simulation isn't just "nice to have"—it is necessary for convergence and robustness. 4/

Tairan He 的头像

Tairan He11 个月前

Bridging the Gap. To enable smooth transfer, we performed System Identification (SysID) on our 3-fingered dexterous hands. The difference is night and day. Below is the overlay of simulation vs. real-world physics before and after SysID. 5/

Tairan He 的头像

Tairan He11 个月前

The Free Lunch. Sim-to-real is hard. But once you solve it, you get generalization for free. VIRAL handles diverse spatial arrangements and visual variations without any real-world fine-tuning. See the stress tests: 6/

Tairan He 的头像

Tairan He11 个月前

The Journey. This wasn't an overnight success. It took 6 months of building visual sim, distributed training, and infra from scratch. Robotics is hard. We’ve documented our failures and our path to success here: 7/

Tairan He 的头像

Tairan He11 个月前

VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation Team: @TairanHe99, Zi Wang, @HaoruXue, @BenQingwei, @zhengyiluo, @_wenlixiao, @_ye_yuan, @XingyeDa, Fernando Castañeda, Shankar Sastry, @ChangliuL, @GuanyaShi, GEAR Leads: @DrJimFan, @yukez Website: Paper:

PrismaX 的头像

PrismaX11 个月前

Training at scale.

Nick 的头像

Nick11 个月前

So, all you need is scale?

Mr 365 的头像

Mr 36510 个月前

Do you know when you'll be able to publish the code? It looks really good and big up all of you! <3

Jed Hoo 的头像

Jed Hoo11 个月前

very very cool!

Fairooz Choudhury 的头像

Fairooz Choudhury11 个月前

Impressive achievement! Excited to learn more.

slicedbread 的头像

slicedbread11 个月前

Kill all robots

David Lin 的头像

David Lin11 个月前

This is insanely impressive. One of the best sim2real zero shot transfer I've seen. Great work team.

奇怪的知识增加了的头像

奇怪的知识增加了11 个月前

Sim2real ftw 🙌

ζ Pedram ζ 的头像

ζ Pedram ζ11 个月前

lol good subject, arbitrarily silly name ..

Forrest Smith 的头像

Forrest Smith11 个月前

What kind of performance did you get from the tiled renderer? At what resolution?

Altiam Kabir 的头像

Altiam Kabir11 个月前

Impressive strides in sim-to-real tech! Excited to see where this leads.

cosmos 的头像

cosmos11 个月前

🫡

Tian Fang 的头像

Tian Fang11 个月前

Very nice work! Did you give any talk in conferences or universities? A good talk will help a lot.

Amy - AI Girl 的头像

Amy - AI Girl11 个月前

Impressive results with VIRAL! The future of robotics is here.

SoloTech 的头像

SoloTech11 个月前

VIRAL is going to push a lot of teams to rethink their pipelines

Sarthak Bhagat 的头像

Sarthak Bhagat11 个月前

Sim2real ftw 🙌

AI PlanetX 的头像

AI PlanetX11 个月前

Impressive work! Excited to learn more about your approach.

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How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

How to use simulation data for real-world robot manipulation? We present sim-and-real co-training, a simple recipe for manipulation. We demonstrate that sim data can significantly enhance real-world performance, even with notable differences between the sim and the real. (1/n)

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46,628 次观看 • 8 个月前

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66,349 次观看 • 7 个月前

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32,979 次观看 • 11 个月前