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. @DynaRobotics aims to change that, with an in-depth look at how scaling works as they approach 1 million hours of training data. @JasonMa2020 @tianyurobot @_anhquanpham and @ChetBhateja joined us to tell us more. They show that as the amount of data they use in pretraining increased, they saw predictable, statistically significant gains on accuracy metrics on held-out data (data not seen during training). They go on to talk about what they learned, and show how this can be applied to many different problems. Watch Episode #99# of RoboPapers, with @micoolcho @chris_j_paxton @DJiafei today to learn more!