THE SPEC SHEET
01 CAPTUREOff-the-shelf rig: RGB, LiDAR depth, hand pose.
| Camera | iPhone Pro, 60fps RGB |
| Depth | LiDAR, metric scale |
| Pose | ARKit 6DoF hand and head |
| Mount | Head or chest |
| Extra views | Fixed cameras, partner set |
| Tactile | Multi-zone force, palm and fingertips. Shear. Wireless, time-synced.ROADMAP |
| Hardware | Off the shelf. Replicable. |
Validated end to end on HOT3D, EgoDex, OpenTouch. 242 tests passing. Transcode fidelity 0.00 reconstruction error, measured on public ground-truth data.
WHAT IT TRAINS
Built for the model classes that learn from human demonstration.
ROBOT FOUNDATION MODELS
Human episodes co-train beside robot data; hand pose stands in as the action stream.
DEXTEROUS HAND POLICIES
Hand pose retargets to multi-finger hands. The closest match to what we record.
WORLD MODELS
Egocentric footage is the mid-training stage between web video and robot data.
IMITATION POLICIES, VIA RETARGETING
Human motion converts to robot trajectories for teleop-native trainers.
Teleop-native policies need robot action labels — human data reaches them through retargeting, not directly.