CERES: Capability-Aware Fast-Slow Underwater Navigation with Corrective Experts

Robots operating with heterogeneous sensing face two coupled deployment decisions: whether a responsive default policy remains inside its capability envelope and, when correction is valuable, which control variable additional computation should revise. Degraded representations, unsafe current actions, and persistent plan failures demand distinct authority and effect times. We present CERES (Capability-Envelope Routing with Expert Specialization), a fast–slow controller that turns these decisions into cost-aware selection among causally typed interventions. System 1 continuously provides a bounded proposal and competence evidence; a coordinator combines learned correction value, realized-outcome memory, and an admissibility contract to preserve fast control or invoke System 2. System 2 centers on two current-decision experts: a State Refiner repairs the representation and a Safety Verifier certifies or projects the action; a Capability Planner conditionally extends correction to subsequent context under verified delayed execution. Across 72 paired trials in three underwater scenes, CERES preserves all 48 routine System 1 successes while recovering 14/24 challenging trials, reaching 86.1% overall success versus 66.7% for System 1 and 72.2% for always-on System 2. It reduces System 1 collision from 16.7% to 1.4% and always-on normalized expert-forward load by 39.5%. At matched success with a near-always-on risk rule, CERES uses 29.6% fewer active experts per decision. Targeted evaluations establish strong representation repair, complementary action-risk correction, verified delayed planning semantics, and a complete fast path below 50 ms at P99.