EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI
For embodied AI researchers, this provides scalable simulation infrastructure to automate environment creation for policy training, reducing manual effort.
EmbodiedGen V2 generates sim-ready 3D environments for embodied AI, achieving 96.5% human acceptance, 98.6% collision success, and 83.3% directly usable task-driven worlds. Online RL in generated environments improves simulation success from 9.7% to 79.8% and real-robot task success from 21.7% to 75.0%.
We present EmbodiedGen V2, a generative 3D world engine for building executable sim-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.