Ep#107: Do As I Do: Dexterous Manipulation Data from Everyday Human Videos

Podcast: RoboPapers · Chris Paxton and Michael Cho

Opublikowano: 2026-09-25

Achieving human-like dexterity is the next frontier for robotics, and yet dexterity data is often subtly hard to scale. Real-world dexterity data, including things like finger-pose estimates, is often slightly off, making it physically invalid and hard to execute on real hardware and hard to learn from. DO AS I DO is an algorithm for reconstructing and retargeting monocular RGB videos to robot hands, outperforming the state of the art and even working from generated videos. Bhawna Paliwal, Haritheja Etukuru, Will Liang, and Mahi Shafiullah join us to tell us more. Watch Episode 107 of RoboPapers...