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