ReliAct: Policy-Relevant Reliability for Robust Underwater Vision-Language-Action Models

Underwater vision-language-action (VLA) models encounter visual degradation and sensor failures in combinations absent from training. We establish an underwater VLA robustness benchmark on U0/USIM with 18 conditions and 3,560 closed-loop episodes, covering visual, sensor, and composite degradation while preserving dynamics and evaluation ground truth. Motivated by state-dependent policy impact and the limitations of fixed visual repair, we introduce ReliAct. It learns policy-relevant reliability from a frozen policy’s response discrepancy under paired clean and corrupted observations. One predicted vector jointly regulates visual repair, multimodal routing, and action generation, without clean references at inference. ReliAct maintains clean performance and raises aggregate corrupted success from 26.7% to 36.0%. With single-source training, it improves success on three unseen composites by 17.1, 14.3, and 13.6 percentage points over U0. Adding 8.6M trainable parameters and 5.9% inference overhead, ReliAct establishes policy-relevant information use as an effective mechanism for robust underwater control across visual degradation, sensor failure, and unseen combinations.