Steve Xie spent years leading simulation at Cruise and NVIDIA before founding Lightwheel — and in that time he watched simulation go from a tool that was "great for showcasing to investors" to what he believes will become the core infrastructure layer for all of physical AI.

In this episode, we sit down with Steve to break down Lightwheel's three-pillar framework for simulation infrastructure: World, Behavior, and Evaluation — and why getting all three right is what separates serious simulation from everything else. We also get into the physical measurement factory, the data scale that Lightwheel is hitting in 2025, and why RoboFinals may become the industry-standard benchmark for frontier robotics models.

In this episode we discuss:
- Why simulation started as a "toy" at Cruise and how Steve changed that.
- The difference between a visually realistic asset and a physically accurate one.
- Why Lightwheel operates one of the world's largest robotics arm factories.
- How egocentric data and simulation data work together in the behavior layer.
- The data pyramid: why real teleoperation is just the tip of the iceberg.
- Why academic benchmarks are maxing out and what RoboFinals does differently.
- How World, Behavior, and Eval form a flywheel — not just a stack.
- The agentic core Steve sees sitting at the center of it all.
- Why robotics data collection may eventually require a billion people.

About Steve Xie:
Steve Xie is the Co-Founder and CEO of Lightwheel. He brings over a decade of experience building simulation infrastructure across some of the most demanding environments in physical AI. Steve led the simulation department at Cruise during the early days of autonomous vehicles, then joined NVIDIA where he worked closely with the Omniverse team and developed his vision for simulation as next-generation physical AI infrastructure. He founded Lightwheel to build that infrastructure from the ground up.

Follow Steve on LinkedIn: https://www.linkedin.com/in/stevexiecbs/

About Lightwheel:
Lightwheel is building the simulation infrastructure that physical AI needs to succeed — spanning world generation, behavior data, and evaluation. Their products include SimReady Assets, EgoSuite for egocentric data collection, and RoboFinals, an industrial-grade robotics evaluation platform co-developed with NVIDIA.

SimReady Assets: https://simready.com/
Learn more at: https://lightwheel.ai/
Resources mentioned in this episode:
LW-BenchHub: https://github.com/LightwheelAI/LW-BenchHub
LeIsaac: https://github.com/LightwheelAI/leisaac
IsaacLab-Arena: http://github.com/isaac-sim/IsaacLab-Arena

Thanks to Lightwheel for making this episode possible. Learn about how Lightwheel is making physical AI successful at https://lightwheel.ai

Robots can walk. They can dance. They can even do backflips.

But can they reliably fold your laundry, make coffee, or recover from mistakes in your kitchen?

In this episode, I sit down with robotics researcher Chris Paxton to talk about what’s actually hard about building intelligent robots.

We explore:

• Why robotics today is fundamentally different than it was 10 years ago
• The rise of world models and robot imagination
• Why contact and manipulation tasks are harder than navigation for robots
• The compounding error problem in long-horizon tasks
• Why robotics evaluation is still an unsolved challenge
• How new data pipelines and egocentric data are accelerating progress
  

If you’ve seen humanoids walking around conferences and wondered, “Are we really close?”, this episode brings clarity.

Follow Chris on X: @chris_j_paxton
Check out RoboPapers for deeper dives into robotics research: https://www.youtube.com/@RoboPapers

Tolga Kart spent seven years building massive 3D worlds for Call of Duty at Sledgehammer Games. Then he left gaming for Tesla Autopilot, led simulation at Parallel Domain, and now he's the CEO of Third Dimension AI, a company building neural simulators that reconstruct reality from sensor data.

In this episode, we dig into SuperSim, Third Dimension's first product, which takes driving logs and reconstructs them into photorealistic 4D environments where robots can train and validate their behavior. The results look so real that Tolga has to convince people they're not just watching video.

In this episode we discuss:
- How SuperSim reconstructs real-world scenes in hours, not months
- The difference between a "digital twin" and a "digital cousin"
- Why procedural generation hit its limits for robotics simulation
- The domain gap problem and why it's finally being solved
- Generating synthetic edge cases: erratic drivers, collapsing bridges, kids running into the street
- Why Gaussian Splatting is a good medium for robotics simulation
- What's next for simulation for humanoids, drones, and beyond

About Tolga Kart:
Tolga Kart is the Co-Founder and CEO of Third Dimension AI. He brings over 2 decades of experience building cutting-edge technology in gaming, autonomy, and AI. Tolga began his career in gaming, shipping two Call of Duty titles at Activision Games before transitioning to autonomous vehicles. At Tesla, he built the Autopilot TPM team and rebuilt the simulation team, fully integrating it into Autopilot's development framework. Most recently, he led and scaled Parallel Domain's engineering organization in two years.

Follow Tolga on LinkedIn: https://www.linkedin.com/in/tolgakart/
Follow Tolga on X: https://x.com/tolgakart

About Third Dimensions AI:
Third Dimension AI is a spatial generation company building the 3D worlds that will power tomorrow's embodied AI—from robots to autonomous vehicles—and enable new frontiers of creativity in gaming and entertainment. Third Dimension was founded in 2024 and backed by venture capital firms Felicis, Abstract, Soma Capital, MVP Ventures, and Solari Capital.

To learn more, visit https://www.thirddimension.ai

Thanks to Lightwheel for making this episode possible. Learn about how Lightwheel is making physical AI successful at: https://www.lightwheel.ai