CD-HSSRL: Cross-Domain Hierarchical Safe Switching Reinforcement Learning Framework for Autonomous Amphibious Robot Navigation

Autonomous tracked amphibious robotic systems operating across water and land environments are essential for coastal inspection, disaster response, environmental monitoring, and complex terrain exploration. However, discontinuous water-land dynamics, unstable medium switching, and safety-critical control under environmental uncertainty pose significant challenges to existing amphibious navigation and path planning methods, where global reachability and adaptive decision-making are difficult to unify. Motivated by these challenges, this paper proposes CD-HSSRL, a Cross-Domain Hierarchical Safe-Switching Reinforcement Learning framework for autonomous tracked amphibious navigation. Specifically, a Cross-Domain Global Reachability Planner is developed to construct unified cost representations across heterogeneous water-land environments, a Hierarchical Safe Switching Policy enables stable medium-transition decision-making through option-based policy decomposition with switching regularization, and a Safety-Constrained Continuous Controller integrates action safety projection and risk-sensitive reward shaping to ensure collision-free control during complex shoreline interactions. These components are jointly optimized in an end-to-end manner to achieve robust cross-domain navigation. Comprehensive experiments on WaterScenes, MVTD, BARN, and Gazebo cross-domain benchmarks demonstrate that CD-HSSRL consistently outperforms state-of-the-art baselines, achieving up to 15% improvement in cross-domain transition success rate and 40% reduction in collision rate. Robustness and ablation studies further verify the effectiveness of hierarchical switching and safety-constrained control mechanisms. Overall, this work establishes a unified solution for safe and reliable cross-domain navigation of tracked amphibious robotic systems, providing new insights into hierarchical safe-switching architectures for multi-medium autonomous robots.