freeze-normalization-statistics-pre-stream-calibration-set TEC-0125 · proposedFreeze normalization statistics from a pre-stream calibratio
Before continual adaptation, collect a handful of task-independent teleoperated trajectories covering the embodiment's anticipated motion range, compute per-dimension 1st/99th percentile action statistics once, and use those frozen statisti
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Before continual adaptation, collect a handful of task-independent teleoperated trajectories covering the embodiment's anticipated motion range, compute per-dimension 1st/99th percentile action statistics once, and use those frozen statisti Filed by the drafting stage from FAN: Foresight Action Normalization for Continual Adaptation of Vision-Language-Action Models (2609.21358) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Average final score across four real-robot streams: 95.3 for FAN vs 77.0 for the best baseline (first-task statistics), 62.0, 59.0 and 25.9 for the others; forward transfer positive on all four streams (avg 9.1 vs -12.7 to -75.6); mean absolute matched-task difference across stream orders 4.2 points vs 17.5-48.5 for baselines. Ablation: narrowing or widening (2.5x) the frozen range cut average score by roughly 24 points on two streams.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
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