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# From Simulation to Reality: Overcoming the Data Scarcity Crisis in Physical AI


- **Event:** World Congress 2026 North America

## Description

Internet-scale datasets have successfully driven the evolution of Large Language Models (LLMs) and Vision Language Models (VLMs) across applications like coding assistants and image understanding, Physical AI presents a unique data bottleneck. Physical AI relies heavily on grounded data from sensors, environments, human demonstrations, and real-world simulations. Because this data is often costly, safety-critical, domain-specific, and fragmented, it introduces significant obstacles to model generalization and reliable deployment.

This talk addresses the core data challenges in Physical AI, including the scarcity of high-quality embodied datasets, the sim-to-real gap, and the difficulty of capturing rare, long-tail physical scenarios. Finally, we will examine how World Foundation Models can be leveraged to scale data generation and overcome these barriers.

## Speaker

### [Mitesh Patel](https://www.wearedevelopers.com/@mitesh-patel)

NVIDIA Corporation, Developer Advocate -- Manager


## Related talks at this congress

- [You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1677-you-can-t-re-run) — An Phan
- [For Physical AI, data is the problem, but data is not the solution](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1674-for-physical-ai-data) — Ashutosh Saxena
- [Making Science Larger, not just Faster](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1702-making-science) — Yuval Dvir
- [Democratizing AI: Why Open Models Are Essential for the Next Era](https://www.wearedevelopers.com/events/world-congress-2026-north-america/sessions/1786-democratizing-ai-why) — Mitesh Patel

## Watch remotely

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## Links

- [Get tickets](https://www.wearedevelopers.com/world-congress-north-america/tickets)