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Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction — arXiv:2608.18840

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18840
License (as archived): CC0-1.0
Archived: 2026-10-18T14:06:37.760Z
SHA-256 of the archived text: 43b2477e7ac13ad24416d751e16f23e93b89ef9c121dfeae1b7cceb1f051cec9
Stable URL of this page: https://metraipolis.com/documento/dc_0da79331d2e5

How to cite

Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction — arXiv:2608.18840 — arXiv (en), https://arxiv.org/abs/2608.18840 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0da79331d2e5 (Metraipolis archive dc_0da79331d2e5, sha256 43b2477e7ac13ad24416d751e16f23e93b89ef9c121dfeae1b7cceb1f051cec9)

BibTeX

@misc{dc0da79331d2e5,
  title = {Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction — arXiv:2608.18840},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18840},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0da79331d2e5; sha256 43b2477e7ac13ad24416d751e16f23e93b89ef9c121dfeae1b7cceb1f051cec9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18840. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.CV).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.18840 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T14:00:22.604Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).