Learning When to Call: Predictive Opportunity Ranking for Embodied Agents

Embodied agents must maintain continuous control while selectively invoking expensive reasoning backends. With a limited call budget, poorly timed requests may miss useful pre-event opportunities. We focus on event-relative opportunity prediction: using causal observations to anticipate pre-onset windows in which to place reasoning requests. The Predictive Reasoning Scheduler (PRS) implements a lightweight scorer from recent state history and current bird's-eye-view context; a separate shared selector enforces budget and cooldown constraints. We evaluate offline exact-call placement on frozen trajectories using Useful-Window Coverage (UWC), the fraction of distinct event windows reached by at least one request. On CARLA Town10HD, a development-selected checkpoint covers 23 of 63 windows with 165 calls, compared with approximately 6 for Random-$k$. The UWC@$0.5\mathrm{s}$ gain is 26.71 percentage points (95% bootstrap interval: 12.64–40.97). On fresh Town10HD scenarios, a model selected using a prespecified development procedure achieves a normalized log-budget UWC-AUC gain of 0.2392 over Random-$k$. Controlled retraining also finds that current-state inputs and broader supervision can outperform the reference design. These findings support event-relative opportunity prediction as a learning formulation for improving the temporal placement of budgeted reasoning calls.