Yunzhu Li

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208 appearances 1 recordings 1 series first heard Jul 2026 last heard 28 Jul

Yunzhu Li’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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The a16z Show · Fei-Fei Li on Spatial Intelligence and Robotics · 28 Jul 2026 podcast

So that is how everything started.

In Cinex, we put together a very, very strong and best teams around robotics, robot learning, and also simulation and rendering, trying to build this real-to-sim to real stack to solve some of the key bottlenecks.

It's amazing that you two work together.

Yeah, so at the very beginning, we were deciding, okay, do we want to just keep going?

But after chatting with Fei-Fei, after seeing all the synergies that happen in the middle, it just makes perfect sense for the forces to join each other.

So in any sense, at Cynics, what we have been doing is real to seem to real, is to do dense reconstruction of the environment.

So we captured the appearance of the environment, geometry of the environment, and also the dynamics of the environment, meaning how the environment is going to change when you apply actions.

So this dense reconstruction right now is still a little bit on the heavier side.

And what World Labs right now has been doing involves a lot of profound capabilities around sparse reconstruction.

and generations.

So we see a lot of opportunities of leveraging Marble and other capabilities as World Labs in order to do very efficient reconstructions and modeling of the environment.

Yeah, great.

So for example, for the foundation models, it essentially needs to be a multimodal model.

So it has to take into account frame text, image, depth, and different kinds of modalities.

And action is a very, very important part of that modality.

So if you think about frame actions as inputs, that is essentially a forward simulator that is going to predict how the environment is going to change when you apply a specific action.

When the action is output, this is essentially a policy model that is trying to predict, give a specific goal, like what should be the action you take in the real environment to get you closer to that goal.

So this kind of omni models actually can benefit a lot and actually provide huge amount of values for the robotics communities in trying to understand how to model the environments and at the same time, how to act.

in the environment.

And this can also act as a backbone for you to fine-tune into specific robotic applications to making sure it really lives up to the reliability and efficiency that's expected by the clients.

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