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arXiv Computation and Language By Dmytro Kurdydyk, Aadi Palnitkar, Sebastian Scherer, Seungchan Kim

SPARK: Self-Correcting Embodied Memory via Physical Re-Observation

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SPARK is a corrective multi‑episode framework for embodied retrieval‑augmented generation that uses a semantic critic, a targeted planner, and selective merging to refine a knowledge base after each episode. In experiments on four AirSim outdoor maps under degraded viewpoints, a single corrective episode improves embodied question‑answering performance on every map while reducing stored objects by 13–41 %. Extending correction to four episodes surpasses the performance of the undegraded reference on all maps, and ablations confirm the importance of each component.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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