OJOx：面向建筑领域具身智能的规格条件化演示
OJOx: Specification-Conditioned Demonstrations for Embodied AI in Construction
- OJOx AI
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出OJOx采集界面，生成规格条件化演示数据，同步记录感知状态、外部设计规格与行为，用于测试具身策略能否依据训练中未见规格行动。
AI中文摘要：
大规模以自我为中心和全身的人体演示正成为具身智能的主要数据来源。它们记录了人们感知和做了什么，但很少记录赋予行动目的的外部规格。在建筑领域，这一遗漏影响重大：技术工作针对的是设计模型中定义的项目特定配置——这些配置在观察到的环境中尚不存在。砌砖工的可迁移能力不是某面墙的几何形状，而是根据规格实现新几何形状的能力。我们引入了规格条件化演示：一种同步记录，包含演示者感知的物理状态、外部设计提供给他们的预期状态，以及连接两者的行为。我们提出了OJOx，一种为此在建筑领域实现的采集界面——将设计几何形状传送到头戴式显示器，将其锚定在物理工作空间中，渲染到演示者的立体透视视图中，并同步记录该视图与全身和手部运动。我们报告了一次完全仪器化的会话——一面由33个组件组成的墙，按照在工作进行中变化的规格铺设——并通过独立于采集注册的外部相机检查记录与物理场景的对应关系。记录的会话与现有的人形重定向基础设施兼容，并可在仿真中重放到Unitree G1上。结果是一种数据接口，用于测试具身策略是否不仅能学习模仿演示的动作，还能针对训练经验中不存在的规格采取行动。
英文摘要：
Large-scale egocentric and whole-body human demonstrations are becoming a primary source of data for embodied intelligence. They record what people perceive and do, but rarely the external specification that gave an action its purpose. In construction that omission is consequential: skilled work is directed at project-specific configurations defined in a design model - configurations not yet present in the environment being observed. A mason's transferable competence is not the geometry of one wall but the ability to realise a new geometry from a specification. We introduce the specification-conditioned demonstration: a synchronised record of the physical state a demonstrator perceives, the intended state supplied to them by an external design, and the behaviour connecting the two. We present OJOx, a capture interface that realises this for construction - delivering design geometry to a headset, anchoring it in the physical workspace, rendering it into a demonstrator's stereo passthrough view, and recording that view synchronously with whole-body and hand motion. We report one fully instrumented session - a 33-component wall laid against a specification that changes while the work proceeds - and check the record against the physical scene through an external camera registered independently of the capture. Recorded sessions remain compatible with existing humanoid retargeting infrastructure and replay onto a Unitree G1 in simulation. The result is a data interface for testing whether embodied policies can learn not merely to imitate demonstrated actions, but to act toward specifications absent from their training experience.
