Arcadia：面向具身终身学习的全生命周期框架
Arcadia: Toward a Full-Lifecycle Framework for Embodied Lifelong Learning
把生成式场景重建与增强当作闭环中的数据引擎，为导航与操作策略持续生成可扩展的仿真训练场景，并用仿真到现实的自适应评估回灌真实数据，这一「生成式仿真更新」环节对做具身视觉与仿真数据扩增的研究者可直接复用。
原文依据 Generative scene reconstruction and augmentation for realistic and extensible scene creation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们认为，具身学习从根本上是一个生命周期问题，而非单阶段优化问题。仅优化其中某一环节（数据采集、仿真、学习或部署）的系统，很少能够持续提升，也难以泛化到狭窄设定之外。我们提出 Arcadia，一个闭环框架，通过紧密耦合四个阶段来将具身终身学习付诸实践：（1）自演化探索与视觉定位，用于在物理环境中自主获取数据；（2）生成式场景重建与增强，用于创建逼真且可扩展的场景；（3）共享具身表征架构，在单一多模态骨干网络内统一导航与操作；（4）从真实到仿真的评估与演化，通过基于仿真的自适应闭合反馈回路。这种耦合是不可分解的：移除任一阶段都会破坏改进回路，并退化为单样本训练。Arcadia 在导航与操作基准上取得了一致的性能提升，并能稳健地迁移到实体机器人，这表明紧密耦合的生命周期——持续的真实世界数据采集、生成式仿真更新与共享表征学习——能够支撑终身改进与端到端泛化。我们发布了标准化接口，使得在可复用环境中进行可复现评估与跨模型比较成为可能，从而将 Arcadia 定位为通用具身智能体可扩展的基础。
英文原文摘要 1486 字符
We contend that embodied learning is fundamentally a lifecycle problem rather than a single-stage optimization. Systems that optimize only one link (data collection, simulation, learning, or deployment) rarely sustain improvement or generalize beyond narrow settings. We introduce Arcadia, a closed-loop framework that operationalizes embodied lifelong learning by tightly coupling four stages: (1) Self-evolving exploration and grounding for autonomous data acquisition in physical environments, (2) Generative scene reconstruction and augmentation for realistic and extensible scene creation, (3) a Shared embodied representation architecture that unifies navigation and manipulation within a single multimodal backbone, and (4) Sim-from-real evaluation and evolution that closes the feedback loop through simulation-based adaptation. This coupling is non-decomposable: removing any stage breaks the improvement loop and reverts to one-shot training. Arcadia delivers consistent gains on navigation and manipulation benchmarks and transfers robustly to physical robots, indicating that a tightly coupled lifecycle: continuous real-world data acquisition, generative simulation update, and shared-representation learning, supports lifelong improvement and end-to-end generalization. We release standardized interfaces enabling reproducible evaluation and cross-model comparison in reusable environments, positioning Arcadia as a scalable foundation for general-purpose embodied agents.