Article

inr2action: Encoding Robot Episodes as Neural Implicit Representations for Recall and Action

The paper proposes the inr2action framework, which fits a neural implicit representation to each robot manipulation episode starting from a shared meta-learned initialization; the resulting weight delta serves as memory, with a size that does not vary with episode length and stays fixed from one hundred frames to one hundred thousand frames.

Part of the event: inr2action Encodes Robot Memory with Fixed-Size Weight Deltas

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