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 suc. Based on arXiv:2607.07459.
Scanned 9/11/2026
Install to Claude Code
npx -y skills add hiyenwong/ai_collection --skill embodiedgen-v2-an-agentic-simulation-ready-3d-world-engine-for-embodied-ai --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Embodiedgen V2 An Agentic Simulation Ready 3d World Engine For Embodied Ai?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-embodiedgen-v2-an-agentic-simulation-ready-3d-worl)More formats (shields.io, HTML) on the badges page.
---
name: embodiedgen-v2-an-agentic-simulation-ready-3d-world-engine-for-embodied-ai
description: '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 suc. Based on arXiv:2607.07459.'
---
# EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI
**arXiv**: 2607.07459 | **Authors**: Xinjie Wang, Liu Liu, Taojun Ding, Andrew Choi, Chaodong Huang et al. | **Utility**: 0.87
## Overview
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.
## Key Contributions
1. We present EmbodiedGen V2, a generative 3D world engine for building executable sim-ready environments for embodied intelligence.
2. 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.
3. 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.
4. The generated environments support manipulation, navigation, mobile manipulation, cross-simulator deployment, and embodied policy training.
## Implementation Notes
- **Keywords**: agentic, policy-optimization, embodied-ai
- **Categories**: cs.RO, cs.CV
- **Published**: 2026-07-08
## Activation Criteria
Use this skill when working on tasks involving: agentic, policy-optimization, embodied-ai.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!