RFW-A: Agentic Procedural Generation of 400K Articulated Assets over 500 Categories

Yuanbo Lyu; Changjian Pan; Saining Zhang; Yi Yang; Peishuo Li; Zhiheng Li; Jialu Liu; Junhao Chen; Jiawei Weng; Yupeng Gao; Weiqing Xiao; Wenyi Li; Jiarui Guo; Rui Zhang; Jingrun Du; Weiyi Lin; Zenan Meng; Hu Wei; Lin Qu; Zongzheng Zhang; Hao Zhao; Chaojian Li

arXiv · 2610.40333

RFW-A: Agentic Procedural Generation of 400K Articulated Assets over 500 Categories

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Abstract

High-quality articulated assets are essential for embodied perception, simulation, and manipulation, yet constructing them at scale remains difficult. Manual annotation and procedural modeling require substantial human effort. Recent agentic systems automate asset construction through program synthesis, but repeat costly reasoning and refinement for every new asset. Our key observation is that assets within a category can share construction logic despite variations in structure and appearance. Agentic synthesis and procedural generation are therefore complementary: agents can construct reusable programs, while procedural execution reuses the resulting logic across many samples. We therefore present RFW-A, which combines the two in a new articulated-asset generation paradigm. Starting from existing articulated programs, RFW-A organizes reusable components and their dependencies into category-level generators. Agents test and refine these generators across sampled configurations and motion states. Once constructed and validated, the generators produce diverse assets through procedural execution with no further model calls or token charges. The estimated API cost of generator authoring averages $10.62, amortizing to $0.05 per asset over 200 assets, versus $1.24 for the sampled per-asset agentic synthesis baseline. Using RFW-A, we built RFW-A-400K, containing over 400K sim-ready articulated assets across more than 500 categories. Experiments show high structural and kinematic validity and stable execution across physics simulators. Beyond asset validity, training with RFW-A-400K improves part-level articulation estimation on seen and unseen categories, while policies trained in simulation with retrieved and program-edited assets transfer zero-shot to real articulated objects.

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Yuanbo Lyu, Changjian Pan, Saining Zhang, Yi Yang, Peishuo Li, Zhiheng Li, Jialu Liu, Junhao Chen, Jiawei Weng, Yupeng Gao, Weiqing Xiao, Wenyi Li, Jiarui Guo, Rui Zhang, Jingrun Du, Weiyi Lin, Zenan Meng, Hu Wei, Lin Qu, Zongzheng Zhang, Hao Zhao, Chaojian Li. 2026-10-29. RFW-A: Agentic Procedural Generation of 400K Articulated Assets over 500 Categories. https://arxiv.org/abs/2610.40333

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