4 papers
Phantom: Training Robots Without Robots Using Only Human Videos
Marion Lepert, Jiaying Fang, Jeannette Bohg
Training general-purpose robots requires learning from large and diverse data sources. Current approaches rely heavily on teleoperated demonstrations which are difficult to scale.…
Masquerade: Learning from In-the-wild Human Videos using Data-Editing
Marion Lepert, Jiaying Fang, Jeannette Bohg
Robot manipulation research still suffers from significant data scarcity: even the largest robot datasets are orders of magnitude smaller and less diverse than those that fueled re…
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair +98
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. Ho…
Shadow: Leveraging Segmentation Masks for Cross-Embodiment Policy Transfer
Marion Lepert, Ria Doshi, Jeannette Bohg
Data collection in robotics is spread across diverse hardware, and this variation will increase as new hardware is developed. Effective use of this growing body of data requires me…