CoDA: Co-Evolution of Data Engines and Action Policies
Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them. We introduce CoDA, a framework in which a simulation data engine and an action policy co-evolve through a shared heterogeneous memory of environment construction, policy failures, and learning progress. The memory compiles validated diagnoses from a vision-language critic into generation programs that recreate difficulties and training programs that help the policy overcome them. We further propose Text2potential to convert these d...
| Authors | Tianjiao Yu, Zhilin Ou, Yifan Shen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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