UC-MESL: A MAP-Elites Skill Library with Hierarchical Policy Switching for Robust Rescue Robotics Under Sensing Uncertainty

Robots deployed in disaster environments—such as collapsed buildings, flooded tunnels, and conflict-damaged urban areas—must navigate without GPS, operate under degraded sensing conditions including dust, smoke, and darkness, and adapt rapidly to changing mission conditions. Most existing learning-based navigation approaches rely on a single policy, which often fails when the environment shifts in unexpected ways. This paper presents UC-MESL (Uncertainty-Conditioned MAP-Elites Skill Library), a framework that learns a diverse library of specialized navigation behaviors and dynamically switches between them in real time based on environmental uncertainty. Each skill is optimized for a specific operating condition and characterized by three interpretable traits: risk tolerance, exploration preference, and movement style. A lightweight selector uses live uncertainty estimates from the robot’s onboard map to choose the most appropriate skill during deployment. We evaluate UC-MESL across three simulated rescue scenarios—collapsed rubble, flooded tunnels, and war-damaged urban blocks—under four levels of sensor degradation and realistic communication outages. Compared with the strongest single-policy baseline, UC-MESL finds 18.4% more victims within the mission time budget, reaches the first victim 31.2% faster, reduces hazard exposure by 24.6%, and loses only 8.3% of performance under severe sensor noise, compared with 29.7% for a single-policy NEAT baseline. These results demonstrate that maintaining a diverse repertoire of specialized navigation skills, combined with uncertainty-aware skill selection, provides more robust and reliable autonomy for disaster-response robotics than optimizing a single general-purpose policy.