发表机构
Meituan（美团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有代码场景生成方法未建模场景功能使用的问题，提出RoomWright框架，通过使用驱动型物体推理与代码智能体实现可执行、可编辑的仿真就绪3D场景，为具身AI与策略学习提供交互式环境。
AI 中文摘要
室内场景合成为具身AI、机器人操纵以及基于仿真的策略学习提供了必要环境。近期基于代码的场景生成方法可生成可编辑、可扩展的环境，但仍聚焦于视觉构建与物体层面的关节运动，基本未对场景的功能使用进行建模。为解决该问题，我们提出RoomWright——一种面向具身交互的、完全以代码表示的使用驱动型智能体框架。RoomWright执行使用驱动型物体推理，将每个锚点视为任务中心，纳入任务所需物体及其可供性。代码智能体通过将每次交互编译为触发器、条件、效果规则来实现多部分交互，该规则会更新结构化物体状态，捕捉物体间的因果依赖关系。此外，由于可操作物体的方向存在歧义且难以从像素中恢复，RoomWright通过注释指导的使用导向型方向缓解了该问题。大量实验证明了我们方法的有效性，生成的场景可执行、可编辑且可用于仿真，为具身AI与策略学习提供了交互式环境。
英文摘要
Indoor scene synthesis provides essential environments for embodied AI, robotic manipulation, and simulation-based policy learning. Recent code-based scene generation methods produce editable and extensible environments, yet they remain focused on visual construction and object-level articulation, leaving the functional usage of scenes largely unmodeled. To address this problem, we present RoomWright, an agentic usage-driven framework for generating 3D scenes represented entirely as code for embodied interaction. RoomWright performs usage-driven object reasoning, which treats each anchor as a task centre and admits task-required objects and their affordances. A code agent further enables multi-part interaction by compiling each interaction into a trigger, condition, effect rule that updates structured object states, capturing causal dependencies across objects. Moreover, since manipuland orientation is ambiguous and hard to recover from pixels, RoomWright alleviates this via annotation-informed usage-guided orientation. Extensive experiments demonstrate the effectiveness of our method. The resulting scenes are executable, editable, and simulation-ready, providing interactive environments for embodied AI and policy learning.
