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SPARK: Self-Correcting Embodied Memory via Physical Re-Observation — arXiv:2611.00344

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00344
License (as archived): CC0-1.0
Archived: 2026-11-03T10:34:08.661Z
SHA-256 of the archived text: 235cbee269a29b959b21a766b125f6c15ee7887b7dc5bbe2dc32857d70b71ca0
Stable URL of this page: https://metraipolis.com/documento/dc_87cb96858deb

How to cite

SPARK: Self-Correcting Embodied Memory via Physical Re-Observation — arXiv:2611.00344 — arXiv (en), https://arxiv.org/abs/2611.00344 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_87cb96858deb (Metraipolis archive dc_87cb96858deb, sha256 235cbee269a29b959b21a766b125f6c15ee7887b7dc5bbe2dc32857d70b71ca0)

BibTeX

@misc{dc87cb96858deb,
  title = {SPARK: Self-Correcting Embodied Memory via Physical Re-Observation — arXiv:2611.00344},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00344},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_87cb96858deb; sha256 235cbee269a29b959b21a766b125f6c15ee7887b7dc5bbe2dc32857d70b71ca0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00344. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Retrieval-augmented generation (RAG) systems have seen growing adoption in embodied settings as language-queryable memories of the environment. However, existing approaches often assume a reliable single-episode knowledge base (KB) or accumulate observations across episodes without correcting prior errors. Such errors can persist through incorrect semantic labels, inaccurate object positions, and inconsistent spatial relationships. To address this, we present SPARK, a corrective multi-episode framework for embodied RAG. After the first episode, a semantic critic flags regions of the KB, a targeted planner generates revisits, and a selective merge adds, removes, or replaces KB information based on the new observations. We evaluate SPARK on four outdoor AirSim maps under two degraded viewpoint conditions, using perception-built KBs, a visibility-constrained oracle used only for evaluation, and a fixed set of 158 embodied question answering (EQA) questions scored before and after correction. A single corrective episode improves downstream EQA on every map under both conditions while storing 13-41% fewer objects than append-only updating, with gains up to 2.7x on one map-condition pair. Extending correction through Episode 4 brings every map above the EQA performance of its undegraded reference. Ablations show that the critic's three signals each contribute to performance, that targeted revisits achieve a larger mean gain than untargeted ones under the same budget, and that selective merging achieves gains comparable to append-only updating with a smaller KB. Overall, SPARK demonstrates the value of correcting embodied memory through targeted re-observation, particularly as embodied systems scale and the risk of accumulated errors increases.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.00344 — 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 2026-11-03T10:29:48.685Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).