Active data collection guided by predictive attribution can reduce the amount of real-world robot teleoperation needed by 2-3x compared to unguided collection, because it focuses on collecting data that actually improves policy performance rather than just semantically relevant data.
GUILD is a method for collecting better training data for robot learning by actively deciding what to collect rather than passively gathering data. It uses vision-language models to suggest relevant tasks and environments, then evaluates which collected data actually helps the robot policy perform better, creating a feedback loop that guides future data collection toward more useful examples.