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

The model doesn't have to be perfect in robotics.

So let me put it this way.

For example, models over the developments of all different kinds of robotic applications has been a very important cornerstone.

If you look at all the existing robotic applications, like Plane, Jones, Roomba, or even for quadruped robots, bipedal robots, model has been the way

for them to actually work and be able to transfer from simulation to the real environment.

But if you look at those locomotion robots, like quadruped robots, bipedal robots, they can walk on snow, they can walk on bushes, but you don't need to have a simulator.

They can simulate all the bushes and snows very precisely.

You need to have a simulation that captures the essential structure of the problem.

and do a whole different kind of randomizations inside the digital environments.

So that is what we're aiming for.

So basically, with Cynics and together with Word Labs, we're trying to investigate what is the level of fidelity we need to model the massive, massive worlds besides the robots, such that we'll be able to transfer the robotic systems training the simulated environment and digital worlds back into the real scenarios.

So they don't contradict with each other.

So if you think about the simulation, simulation is essentially trying to predict how the environment is going to change when you apply the actions.

And this is essentially a model of the world that doesn't necessarily have to be pure physics.

It can be a combination between both physics and also learning.

We are collecting real-world data.

We will be using those real-world data.

It's just at different stages of this, like a data flywheel.

Maybe at the very beginning, we have stronger emphasis on we have more physics to making sure we have the right consistency and right structure for us to learn the world, for us to train the robot policies.

But as we accumulate more and more data, both through data collection and also through the collaboration with our clients, we'll have the data that will be moving more towards more learning-based

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