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This arXiv preprint proposes storing robot episodes as fixed-size weight deltas from a meta-learned neural implicit representation, which a language model and a command decoder then read. The reported results are modest: a mean R² of 0.51 on RoboMME, and an 11.4-point drop when the delta is replaced with noise. The most useful signal for readers is the ablation showing that the shared meta-learned initialization is necessary, which suggests the approach's scalability claims still need testing on longer and more varied streams.
inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action
arXiv:2610.28882v1 Announce Type: new 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.28882 [cs.RO]
(or arXiv:2610.28882v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.28882 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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