CoDA：数据引擎与动作策略协同进化的机器人训练框架
CoDA: Co-Evolution of Data Engines and Action Policies
机器人训练效率有新思路：CoDA 让仿真环境和策略一起进化，LIBERO-Pro 上指令扰动成功率从 1% 拉到 76%，做具身智能的可以看看这篇论文。
arXiv 论文提出 CoDA 框架，让生成式仿真数据引擎与动作策略通过共享异构记忆协同进化，记录环境构建、策略失败和学习进度。框架引入 Text2potential 方法，将视觉语言批评器输出的诊断转化为分阶段势能，为动作块价值学习提供密集监督。在四个操作任务族上，CoDA 将分布外成功率从监督微调后的 5.3% 提升到 27.6%，比最强闭环基线高 9.5 个百分点。在 LIBERO-Pro 基准上，15 轮生成与训练将位置扰动下的成功率从 25% 提到 84%，指令扰动下从 1% 提到 76%。
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 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.
