SceneTeract：代理功能可能性与视觉语言模型在3D场景中的 grounded 性
SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes
AI总结：
SceneTeract 通过结合高层语义推理与低层几何检查，验证3D场景功能，评估合成环境中的功能失败及VLM对功能可能性的预测能力，揭示语义信心与物理可行性之间的系统性不匹配。
摘要：
具身AI依赖于支持多样化用户有意义活动的交互3D环境，但评估其功能可能性仍是核心挑战。我们介绍了SceneTeract框架，该框架在代理特定约束下验证3D场景功能。核心贡献是结合高层语义推理与低层几何检查的 grounded 验证引擎。SceneTeract将复杂活动分解为原子动作序列，并针对可访问性要求（如可达性、 clearance 和可导航性）进行验证，使用显式物理和几何模拟。我们部署SceneTeract对合成室内环境进行深入评估，揭示频繁的功能失败，以及前沿视觉语言模型（VLMs）在推理和预测功能可能性时的系统性不匹配。最后，我们利用SceneTeract作为VLM后训练的奖励引擎，实现几何约束的可扩展蒸馏。我们发布SceneTeract验证套件和数据，以弥合感知与物理现实之间的差距。
英文摘要：
Indoor 3D scenes are ultimately meant to be used: an embodied agent should be able to navigate, reach objects, and complete diverse activities. Yet whether a given scene actually supports these activities for a specific agent profile is rarely verified. Existing evaluations of indoor 3D scenes typically focus on visual quality and semantic plausibility. In contrast, the feasibility of an activity depends on geometric, agent-specific constraints such as reach, clearance, and navigable space availability. These properties are not captured by visual plausibility metrics, and, as we show, VLMs, which are increasingly used to reason about 3D scenes, often fail to determine action feasibility in a single shot. We present SceneTeract, a verification interface that separates semantic action understanding from physical feasibility. Given a scene, an activity, and an embodied agent profile, we decompose the activity into atomic actions on scene objects. Explicit geometric checks then decide whether each step is executable and return a diagnostic trace explaining failures. In synthetic indoor scenes, SceneTeract reveals widespread functional and accessibility failures across diverse agent profiles. Moreover, when benchmarked against our verification, we find that existing VLMs systematically over-predict action feasibility, highlighting limited awareness of embodied functional constraints. In response, we post-train a lightweight VLM with verifier feedback, improving its assessment of physical feasibility. Although trained only on renders of synthetic scenes, we demonstrate that scene understanding improvements also generalize to real-world scenes. We will release our verification suite, benchmark labels, and diagnostic trace datasets.
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