Task Planning for Robotic Disinfection Using Generative-Adversary-Trimodel (GAT)

Abstract

Robotic disinfection can relieve human operators from repetitive, labor‑intensive tasks while reducing the risk of pathogen transmission in public spaces. Recent advances in learning-based methods further enhance these systems by enabling robust dynamic task planning and the interpretation of ambiguous instructions. However, disinfection task planning remains a four-dimensional (interaction, logic, spatial and temporal) problem that requires expert knowledge. The robust task planning for autonomous disinfection in dynamic environment remains challenging. This paper proposes a novel framework that integrating the Generative Adversarial Trimodel (GAT) method with embodied framework to solve the four-dimensional problem in the dynamic environment. The GAT method injects expert knowledge and iteratively refines neural network-generated plans against analytical model (AM), driving dual convergence and reducing logic, spatial, and temporal errors. By combining embodied framework and the GAT method into a GAT-enhanced embodied framework, the robot system autonomously perceives objects of unknown shape and pose, long-horizon task sequence plans, and executes disinfection operations. Experimental results demonstrate an improvement in success rate and reduce the average task time and rule violation rates compared with non-GAT methods, demonstrating improved robustness and efficiency in dynamic environment.