English(EN) SceneBloom: Agentic Indoor Environment Generation with Realistic and Diverse Layouts for Embodied Learning

SceneBloom框架为具身AI训练生成逼真的室内环境

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了SceneBloom，一个新颖的代理式框架，旨在为训练具身AI代理生成逼真且多样化的室内环境。该系统集成了视觉布局采样与合同引导的3D编译，使用扩散先验进行家具布置，并利用关系提取进行几何拟合。据报道，SceneBloom比现有方法能生成更高质量、更物理稳定的场景，没有检测到网格碰撞或越界对象，并且其生成的布局更接近真实房间。在SceneBloom环境中训练的代理在真实世界扫描上的表现优于在实际房间数据上训练的代理。 AI

影响 SceneBloom逼真的环境生成能力有望加速机器人和模拟领域具身AI代理的开发和泛化能力。

排序理由这是一篇详细介绍AI环境生成新框架的研究论文。

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuhan Liu, Lianhui Qin, Shenji Wan · 2026-10-26 04:00

SceneBloom：用于具身学习的具有逼真且多样化布局的智能室内环境生成

arXiv:2610.32978v1 Announce Type: cross Abstract: Training generalizable embodied agents requires realistic, varied, and simulation-ready indoor environments. Language-guided methods support semantic planning and object-level reasoning, but precise spatial grounding remains diffi…

本文如何被排名

报道来源 [1]

SceneBloom：用于具身学习的具有逼真且多样化布局的智能室内环境生成

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