The data bottleneck and need for a shared baseline

In his first sit-down interview, Spencer Huang – NVIDIA’s product lead for robotics software – talks about his role at NVIDIA, a flat organization where “you have access to everything.” We discuss how open source shapes NVIDIA’s robotics ecosystem, how robots learn physics through simulation, and why neural simulators and world models may evolve alongside conventional physics. I also ask him what’s harder: working on robotics or being Jensen Huang’s son.

Watch to learn a lot about robotics, NVIDIA, and its big plans ahead. It was a real pleasure chatting with Spencer.

*We cover:*

- NVIDIA’s big picture

- The “three computers” of robotics – training, simulation, deployment

- Isaac Lab, Arena, and the path to policy evaluation at scale

- Physics engines, interop, and why OpenUSD can unify fragmented toolchains

- Neural simulators vs conventional simulators – a data flywheel, not a rivalry

- Safety as an architecture problem – graceful failure and functional safety

- Synthetic data for manipulation – soft bodies, contact forces, distributional realism

- Why the biggest bottleneck is robotics data, and how open ecosystems help reach baseline

- NVIDIA’s “Mission is Boss” culture – cross-pollinating research into robotics

This is a ground-level look at how robots learn to handle the messy world – and why simulation needs both fidelity and diversity to produce robust skills.

*Chapters*:

0:22 The future of Physical AI begins here

1:00 Inside NVIDIA’s secret blueprint for teaching robots

3:46 Why safety is the hardest part of robotics

4:11 Simulation: the new classroom for machines

8:55 Can robots really understand physics?

13:55 How NVIDIA builds robot brains without a PhD

16:47 The plan to unify a fragmented robotics world

20:31 Why open source is NVIDIA’s biggest power move

21:21 What’s harder – robotics or being Jensen Huang’s son?

24:31 The one thing holding robotics back

27:56 The sci-fi books that shaped Spencer's mind

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*Guest:* Spencer Huang, NVIDIA – a product line manager at NVIDIA leading robotics software product. His work centers on open-source simulation frameworks for robot learning, synthetic data generation methodologies, and advancing robot autonomy – from industrial mobile manipulators to generalist humanoid robots.

https://www.linkedin.com/in/spencermhuang/

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#robotics #simulation #NVIDIA #Omniverse #digitaltwins #worldmodels #physicalAI #reinforcementlearning #syntheticdata