A robot that performs in the lab will fail in the real world unless it’s been trained on the right data. Life is faster and more unpredictable than any test environment, and the model needs a clear understanding of how people move and environments shift.
The challenge is that physical behavior data cannot be scraped from the internet. Only egocentric data captured in motion and in context provides the high-fidelity ground truth robots need to work reliably.
To help build and scale this foundation, we put together a white paper covering:
- How to capture quality, first-person perspective
- Ensuring models operate correctly in different regions and settings
- The importance of human-in-the-loop operations from training to deployment
