CoDA: Co-Evolution of Data Engines and Action Policies

Tianjiao Yu, Zhilin Ou, Yifan Shen, Xinzhuo Li, Yuanzhe Liu, Onkar Kishor Susladkar, Fan Wang, Ismini Lourentzou · N/A · 2026

Summary

Abstract Summary

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 diagnoses into staged potentials for action-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.

Key Points

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 diagnoses into staged potentials for action-chunk value learning, providing dense supervision for policy updates while preserving the task's optimal policy under standard shaping assumptions.

Method

0.64314 0.68627 0.84706C0.34902 0.7451 0.88235o \__color_backend_reset: \__color_backend_reset: 0.44706 0.78039 0.59608D0.5451 0.81176 0.3098A \__color_backend_reset: \__color_backend_reset: couples scene generation and policy learning through a shared memory that guides both. A data engine $\mathcal{G}$, implemented as a tool-augmented coding agent, writes executable scene programs against a simulator SDK.

Each program defines an environment and a machine-checkable success predicate $\nu$.

Method — CoDA: Co-Evolution of Data Engines and Action Policies — Figure 1 : The 0.64314 0.68627 0.84706C0.34902 0.7451 0.88235o \__color_backend_reset: \__color_backend_reset: 0.44706 0.78039 0.59608D0.5451 0.81176 0.3098A \__color_backend_reset: \__color_backend_reset: framework. The data engine $\mathcal{G}$ generates executable scenes with independently editable task, spatial, and object-attribute factors $(\tau,\sigma,\alpha)$ and a sparse success verifier $\nu$. Construction traces and rollout-grounded failure diagnoses enter a shared memory, which organizes them into reusable difficulty modes. Validated modes are compiled into generation programs that reproduce the diagnosed conditions and training programs that specify subgoals, potentials, and learning tools. Text2potential supplies dense feedback through chunk-level value targets that guide policy updates, and measured learning progress guides the next generation round.

Experiments

Experimental Setup. We evaluate CoDA on four manipulation task families and LIBERO-Pro [ 52 ] using $\pi_{0.5}$ [ 8 ] as the primary policy backbone. Our main comparison includes the released $\pi_{0.5}$ checkpoint, supervised fine-tuning on seed demonstrations, and policies trained with domain randomization, uniform environment variants [ 29 ] , or success-rate-guided generation [ 10 ] .

Sources and Demonstrations

Figures are reproduced from Yu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.