Requery Is Not Free: Cross-Query Coupling in Generative Robot Control

Action-chunked policies have become a widely adopted and effective approach to robot control. These policies repeatedly generate new action chunks from fresh observations, allowing them to adapt during execution. Yet each new chunk must continue a trajectory shaped by earlier actions. Fresh observations update what the policy sees, but may not reveal how that trajectory was reached. A new chunk may therefore fit the current observation yet be incompatible with the execution already underway. We formalize this dependence as cross-query coupling and prove that identical per-query chunk distributions can yield exponentially different trajectory success. In learned policies, we find that a chunk's value depends on the physical trajectory it continues, with particularly pronounced effects in some tasks and execution stages. This diagnosis motivates Stateful Requery, which combines fresh observations with a compact record of realized execution. The record preserves what earlier actions actually changed, giving each new generation context for continuing the current trajectory. The method improves control across all tested backbone–generator combinations on Meta-World MT50 and LIBERO-90. Analysis shows how the record guides branch-specific action adjustments. It remains useful even when the policy requeries after every action. These findings identify requery as a trajectory-composition problem, where effective control requires action chunks that are not merely plausible but appropriate for the execution they continue.