[Submitted on 20 Oct 2026]
Title:inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action
View a PDF of the paper titled inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action, by Leann Alhashishi and 2 other authors
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Abstract:Long-horizon robotic operation requires memory that scales with essential information rather than execution time. We present a parameter-space memory framework where each episode is stored as a separable object. A neural implicit representation is fitted to episode video from a shared, meta-learned initialization; the resulting weight delta constitutes the memory. Crucially, this delta remains a fixed size regardless of whether the episode spans a hundred or a hundred thousand frames. A learned projector maps the weight delta into token embeddings, conditioning a language model for question answering and a decoder for joint command regression, without frame retrieval at query time. On RoboMME, the language model exceeds chance across all question types, and the decoder achieves a mean $R^2$ of 0.51. Replacing the delta with matched noise drops performance by 11.4 points, while random-origin weights are non-decodable, proving the meta-learned initialization is essential for embedding episode content. By representing individual experiences as fixed-size weight deltas in a common parameter space, this approach decouples memory cost from time horizon and provides a basis for scalable streaming memory.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.28882 [cs.RO]
(or arXiv:2610.28882v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.28882
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Christos Margadji [view email] [v1] Tue, 20 Oct 2026 20:00:50 UTC (1,840 KB)
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View a PDF of the paper titled inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action, by Leann Alhashishi and 2 other authors
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