This research addresses the domain discrepancy between human and robot data for training effective robotic manipulation systems.

• Introduces techniques to leverage large-scale human activity data despite morphological differences
• Develops methods to mitigate the human-robot gap in visual pre-training
• Enables more generalizable visual representations across embodied environments
• Addresses the challenge of limited robot demonstration data

For engineering teams, this approach offers a pathway to more robust robotic systems that can better understand and interact with real-world environments while requiring less specialized training data.

Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation