[Submitted on 13 Sep 2026]
Title:OJOx: Specification-Conditioned Demonstrations for Embodied AI in Construction
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Abstract: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.
Comments: 10 pages, 6 figures. Project website: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.22289 [cs.RO]
(or arXiv:2609.22289v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.22289
arXiv-issued DOI via DataCite
Submission history
From: Mohamed Dawod [view email] [v1] Sun, 13 Sep 2026 21:35:06 UTC (4,633 KB)
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