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CoDA co-evolves simulation data and policies to address robot failures
CoDA turns diagnosed policy failures into regenerated training environments and staged learning signals; on four manipulation task families, it raises out-of-distribution success from 5.3% to 27.6%. The results show how targeted environment generation can improve embodied-agent robustness.
- What
- CoDA co-evolves a simulation data engine and an action policy to address robot policy failures.
- Method
- A shared heterogeneous memory turns diagnoses from a vision-language critic into generation and training programs; Text2potential makes staged potentials for action-chunk value learning.
- Why it matters
- Measured learning progress guides further generation and program refinement, linking simulation data production to a robot policy's observed needs.
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