Preference-Conditioned Sequential Optimization for Multi-Target Cleanup in Action-Dependent Risk Fields

Radioactive source cleanup in nuclear environments is an important task for facility decommissioning and autonomous radiation management. In multi-target cleanup scenarios, each source removal action alters the subsequent radiation risk distribution. This action-dependent evolution invalidates the static planning assumptions commonly adopted in conventional routing and path planning methods. To address this challenge, this work formulates multi-target radioactive hotspot cleanup as a preference-conditioned sequential combinatorial optimization problem. We propose a neural sequential optimization framework that integrates hotspot map encoding and radiation field image encoding through cross-modal interaction. The proposed framework generates preference-conditioned risk-aware cleanup policies, enabling effective coordination between target selection and safe navigation under different risk–efficiency trade-offs. The nuclear hot-cell simulation environment is constructed using the Robot Operating System (ROS) and the Gazebo physics simulation engine. Compared with classical heuristic methods, evolutionary optimization methods, and reinforcement learning-based baselines, the proposed method achieves lower cumulative radiation exposure across different problem scales while maintaining competitive task efficiency. The results demonstrate that the proposed framework provides an effective task-level sequencing strategy for radiation-aware robotic cleanup in action-dependent risk fields.