EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation

原文：EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation

作者：Yikai Qin, Yifei Deng, Mingjian Liang, Wenxuan Song, Zepeng Lin, Zhiyi Jiang, Jiajun Fu, Qiao Sun, Huashuo Lei, Xicheng Gong, Jiayi Chen, Han Zhao, Shuanghao Bai, Pengxiang Ding, Pengwei Wang, Haoang Li

来源：arXiv cs.RO（机器人）

正文

Computer Science > Computer Vision and Pattern Recognition

arXiv:2610.07969v1 (cs)

[Submitted on 6 Oct 2026]

Title:EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation

Authors:Yikai Qin, Yifei Deng, Mingjian Liang, Wenxuan Song, Zepeng Lin, Zhiyi Jiang, Jiajun Fu, Qiao Sun, Huashuo Lei, Xicheng Gong, Jiayi Chen, Han Zhao, Shuanghao Bai, Pengxiang Ding, Pengwei Wang, Haoang Li

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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.

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

Cite as:

arXiv:2610.07969 [cs.CV]

(or

arXiv:2610.07969v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.07969

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arXiv-issued DOI via DataCite (pending registration)

主题

计算机视觉 · 机器人

由「前沿雷达」于 2026-10-06 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。