EmbodiedSmith：透過模擬中的遞迴自我改進飛輪擴展具身數據
EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation
EmbodiedSmith 提出一套模擬框架，透過遞迴自我改進擴展具身數據生成，並整合資產、場景和任務生成。其智能體改進循環讓場景與任務互相引導修訂，並支援移動操作機械人、人形機械人、靈巧手，以及涉及可變形物件和流體的互動。實驗顯示，增加數據多樣性可提升下游策略的泛化能力；框架亦可用於機械人預訓練和評估。
Published on Oct 6
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Abstract
Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existing generation pipelines remain constrained by predefined assets and skills, a disconnect between scene generation and task generation, and limited support for complex embodiments and physics. We introduce EmbodiedSmith, a framework for scalable embodied data generation through recursive self-improvement (RSI). EmbodiedSmith unifies asset, scene, and task generation in a pipeline that supports autonomous creation and language-driven customization. Its core is an agentic refinement loop: scene generation anticipates downstream task requirements, while task generation guides targeted scene edits, allowing scenes and tasks to iteratively improve one another. This joint refinement improves task generation success, including for long-horizon tasks. The framework further supports mobile manipulators, humanoids, and dexterous hands, as well as interactions involving deformable objects and fluids, broadening the range of behaviors and physical phenomena represented in generated data. Together, these capabilities provide a flexible simulation engine for both robot pretraining and evaluation. Extensive experiments validate the quality, diversity, and generation efficiency of the resulting data, while downstream policy experiments demonstrate that increased data diversity improves generalization.
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來源：HuggingFace Daily Papers（社區熱門論文） · huggingface.co