Robot Learning
Dobb-E
Dobb-E is an open-source framework from NYU (2023) for teaching mobile manipulators household tasks from brief human demonstrations. Data is collected with the Stick, a reacher-grabber tool carrying an iPhone, yielding the Homes of New York dataset of thousands of demonstrations across dozens of real homes. A visual representation pretrained on this data lets a Hello Robot Stretch learn new home tasks from roughly five minutes of demonstrations with high success rates.
Why it matters for physical AI
Cheap, phone-based data collection in real homes demonstrated a practical alternative to lab teleoperation for scaling household manipulation data, influencing subsequent low-cost data-gathering tool designs.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.