This paper presents a Verbal Reinforcement Learning framework for interpretable task-level planning in mobile robots operating under execution uncertainty. The approach employs a closed-loop architecture where Behavior Trees are iteratively refined by a Large Language Model, guided by structured feedback from a Vision-Language Model critic that observes robot execution. Unlike traditional reinforcement learning, policy updates occur at the symbolic planning level without gradient-based optimization, enabling transparent reasoning and human-interpretable policy evolution. The framework is validated on a real mobile robot performing multi-stage manipulation and navigation tasks, demonstrating effective handling of execution uncertainty through iterative refinement.