Ep#99: DYNA-2: A 1 Million Hour Scaling Law for World-Action Models

Podcast: RoboPapers · Chris Paxton and Michael Cho

Opublikowano: 2026-08-18

Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete demonstration of scaling in robot learning. Dyna Robotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. Jason Ma, Tianyu Li, Anh-Quan Pham, and Chet Bhateja joined us to tell us more. They show that as the amount of data they use in p...