By treating robot learning as active hypothesis testing rather than passive data collection, you can dramatically reduce the physical experiments needed to improve foundation models—this system reaches 77% success where baselines only achieve 40%.
EmbodiedRSI is a self-evolving robot control system that autonomously decides which experiments to run on physical robots and uses the results to improve its code and skills.