Co-evolving data generation and policy training through shared failure diagnosis and staged learning signals can dramatically improve robot learning efficiency and robustness to real-world variations.
CoDA is a framework where a robot learning system and a data generation engine improve together by sharing a memory of failures and solutions. A vision-language critic diagnoses why policies fail, and this feedback guides both the creation of challenging training environments and the design of better learning signals.