Safe Constrained Reinforcement Learning for Maintenance Robotics: Integrating Dual-Policy Frameworks with Domain Adaptation for Hazardous Environment Operations

Abstract

Building robots that safely inspect offshore wind turbines or clean solar panels in extreme environments is difficult because training in simulation doesn't automatically work in reality. We examined how constrained safe reinforcement learning combined with domain adaptation helps robots handle hazardous maintenance tasks without catastrophic failures. By reviewing recent robotics literature, we found that using two policies together works best: one optimizing task performance and another enforcing hard safety boundaries like preventing collisions and excessive speeds. This dual-policy system significantly reduces dangerous mistakes during sim-to-real transition. However, solving this properly means addressing three interconnected challenges: bridging the gap between simulated and real-world perception with changing lighting and occlusions, learning to handle unexpected environmental conditions while never breaking safety rules, and improving from field experience without violating safety constraints. We can provide mathematical guarantees about robot safety in new situations using formal verification, but running dual policies simultaneously strains smaller robotic platforms. Adaptation strategies create trade offs because cautious learning is slower, and we haven't fully solved how safety guarantees transfer across domains. Real solutions require designing safety directly into task objectives rather than separately, accounting carefully for environmental uncertainty, and creating protocols that gradually expand capabilities while maintaining verified safety. This work bridges the critical gap between laboratory validation and real-world autonomous operation for high-stakes industrial applications.