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arXiv · 2607.07459

EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI

Xinjie Wang·Liu Liu·Taojun Ding·Andrew Choi·Chaodong Huang·Mengao Zhao·Ziang Li·Jackson Jiang·Chunlei Yu·Shengxiang Liu·Wei Xu·Zhizhong Su
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Abstract

We present EmbodiedGen V2, a generative 3D world engine for building executable policy-ready environments for embodied intelligence. Sim-ready 3D asset generation has advanced rapidly, yet assembling such assets into policy-ready task environments remains largely manual, limiting scalable closed-loop learning. EmbodiedGen V2 addresses this gap through a unified sim-ready representation that connects cross-simulator assets, interaction affordances, task-driven worlds, large-scale multi-room scenes, and stateful Vibe Coding into a generative, editable, and reusable simulation pipeline. The generated environments support manipulation, navigation, mobile manipulation, cross-simulator deployment, and embodied policy training. In evaluation, the asset pipeline achieves 96.5% human acceptance and 98.6% collision success, and 83.3% of task-driven worlds are directly usable for downstream simulation without manual modification. Online reinforcement learning with generated environments further improves simulation success from 9.7% to 79.8%, and transfers to real robots with task success increasing from 21.7% to 75.0%. These results establish EmbodiedGen V2 as scalable simulation infrastructure for training, evaluating, and deploying embodied policies.

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Xinjie Wang, Liu Liu, Taojun Ding, Andrew Choi, Chaodong Huang, Mengao Zhao, Ziang Li, Jackson Jiang, Chunlei Yu, Shengxiang Liu, Wei Xu, Zhizhong Su. 2026-07-08. EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI. https://arxiv.org/abs/2607.07459

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