Paper page - CoDA: Co-Evolution of Data Engines and Action Policies

Source: https://huggingface.co/papers/2610.22362

Abstract

CoDA co-evolves a simulation engine and robot policy via shared memory of failures and progress, using vision-language diagnostics and staged potential rewards to improve out-of-distribution manipulation success.

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 sharedheterogeneous memoryof environment construction, policy failures, and learning progress. The memory compiles validated diagnoses from avision-language criticintogeneration programsthat recreate difficulties and training programs that help the policy overcome them. We further proposeText2potentialto convert these diagnoses into staged potentials foraction-chunk value learning, providing dense supervision for policy updates while preserving the task’s optimal policy under standard shaping assumptions. Measured learning progress guides further generation and program refinement. Across four manipulation task families, CoDA improves out-of-distribution success from 5.3% after supervised fine-tuning to 27.6%, exceeding the strongest closed-loop baseline by 9.5 percentage points with the same generation budget. On LIBERO-Pro, 15 rounds of generation and training raise success from 25% to 84% under position perturbations and from 1% to 76% under instruction perturbations.

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