编辑即行动：面向开放词表3D室内场景编辑的目标回归规划
Edit-As-Act: Goal-Regressive Planning for Open-Vocabulary 3D Indoor Scene Editing
将自然语言指令转化为3D空间中的符号化动作序列，通过PDDL启发的动作语言显式编码支撑、接触、碰撞等几何关系，为开放词表3D场景编辑提供可解释、物理一致的规划框架，可复用于具身智能中的场景操作与交互。
原文依据 performs open-vocabulary scene editing as goal-regressive planning in 3D space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从自然语言编辑3D室内场景在概念上直接，但在技术上具有挑战性。现有的开放词表系统通常重新生成场景的大部分内容，或依赖会破坏空间结构的图像空间编辑，导致意外的全局变化或物理上不一致的布局。这些限制源于将编辑主要视为生成任务。我们采取不同的观点。用户指令定义了期望的世界状态，而编辑应该是使该状态成立同时保留其他一切的最小动作序列。这一视角催生了Edit-As-Act，一个在3D空间中作为目标回归规划执行开放词表场景编辑的框架。给定源场景和自由形式指令，Edit-As-Act预测符号化目标谓词，并在EditLang——我们设计的受PDDL启发的动作语言——中进行规划，该语言具有显式的前置条件和效果，编码支撑、接触、碰撞及其他几何关系。语言驱动的规划器提出动作，验证器强制执行目标导向性、单调性和物理可行性，产生可解释且物理一致的变换。通过将推理与低级生成分离，Edit-As-Act实现了指令保真度、语义一致性和物理合理性——这三个标准是现有范式无法同时满足的。在E2A-Bench（我们的基准，包含9个室内环境中的63个编辑任务）上，Edit-As-Act在所有编辑类型和场景类别上显著优于先前方法。
英文原文摘要 1572 字符
Editing a 3D indoor scene from natural language is conceptually straightforward but technically challenging. Existing open-vocabulary systems often regenerate large portions of a scene or rely on image-space edits that disrupt spatial structure, resulting in unintended global changes or physically inconsistent layouts. These limitations stem from treating editing primarily as a generative task.We take a different view. A user instruction defines a desired world state, and editing should be the minimal sequence of actions that makes this state true while preserving everything else. This perspective motivates Edit-As-Act, a framework that performs open-vocabulary scene editing as goal-regressive planning in 3D space.Given a source scene and free-form instruction, Edit-As-Act predicts symbolic goal predicates and plans in EditLang, a PDDL-inspired action language that we design with explicit preconditions and effects encoding support, contact, collision, and other geometric relations. A language-driven planner proposes actions, and a validator enforces goal-directedness, monotonicity, and physical feasibility, producing interpretable and physically coherent transformations.By separating reasoning from low-level generation, Edit-As-Act achieves instruction fidelity, semantic consistency, and physical plausibility--three criteria that existing paradigms cannot satisfy together. On E2A-Bench, our benchmark of 63 editing tasks across 9 indoor environments, Edit-As-Act significantly outperforms prior approaches across all edit types and scene categories.