The Fidelity Floor: What Manipulation Data Must Preserve
Not all motion data is useful for robot learning. Manipulation datasets must preserve the physical signals that define how an action actually happens—from finger articulation to precise 6-DoF trajectories and contact events.
Distribution Beats Volume: Capturing Beyond the Lab
A thousand demonstrations in one controlled environment can teach less than a smaller dataset collected across diverse real-world conditions. Physical AI needs distribution, not just volume.
The Physical AI Data Stack: From Capture to Robot Learning
Building capable physical AI requires more than sensors and models. A scalable data stack must connect human behavior, multimodal capture, synchronization, validation, and machine-learning infrastructure.