ROBOT MEMORY IN CONTEXT: SEPARATING STORED CONTENT FROM CONDITIONS OF USE

After a cue disappears, a robot must still choose the object it indicated. We ask when a stored representation that can guide this choice remains useful as the scene and control phase change. In a frozen recurrent robot policy, we replace saved states and edit cue components estimated from other source scenes, holding the current test scene fixed within each comparison. On independently sampled scenes, replacing one cue component with another redirects 53/64 choices toward the specified new object, versus 0/64 for token-magnitude-matched random changes: an 82.8-percentage-point gain in targeted redirection. The same edited tensors yield 11/64 matches when inserted two actions later, with the same remaining action budget. What the robot did in between matters: at that later query, an unchanged saved state guides 14/64 choices after the ordinary action prefix and 49/64 when the two preceding arm motions are paused. On jointly new source and test scenes, calibration reduces Brier probability error from .1717 to .0583, a 66.0% reduction against a same-budget rate baseline. A memory can hold the right content yet fail to guide action, so robot memory should be evaluated both for what stored content can direct and for when the controller can use it.