When Memory Updates Help Prediction but Hurt Control: A Flight Dynamics Study

Robots use recent observations to predict how they will respond to future actions. During planning, however, imagined actions supply no new observations. Should a dynamics model keep the memory inferred from observed history fixed, or update it along its own predicted trajectory? We study this question in quadrotor simulation using matched, separately trained Frozen and Updated memory designs under a common planner. With the original logged-training recipe and no layer normalization (LN), Updated improves logged prediction, including physical position, yet produces worse closed-loop tracking. Replacing logged actions with planner candidates reverses the mean prediction ranking even when histories and initial physical states are held fixed. We further show that training recipe and optimization budget can reverse which design controls better. At the long budget, Mixed training with candidate actions improves Updated’s tracking but worsens Frozen’s relative to Re-simulated training with logged actions. A prospective test on five new datasets confirms these opposing effects at both extrapolation winds. These findings show that memory design, training recipe and optimization bud- get jointly shape the link between prediction and control. They motivate assessing robot dynamics models on logged prediction, planner queries and closed-loop control before choosing how memory should evolve during planning.