Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning

• URL: http://arxiv.org/abs/2603.22169v1
• Date: Mon, 23 Mar 2026 16:28:36 GMT
• Title: Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning
• Abstract summary: We propose a new Verbal Reinforcement Learning framework for interpretable task-level planning in mobile robotic systems.<n>The framework follows a closed-loop architecture that enables iterative policy improvement through interaction with the physical environment.<n>We validate the proposed framework on a real mobile robot performing a multi-stage manipulation and navigation task under execution uncertainty.
• Score: 1.6641413441634143
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose a new Verbal Reinforcement Learning (VRL) framework for interpretable task-level planning in mobile robotic systems operating under execution uncertainty. The framework follows a closed-loop architecture that enables iterative policy improvement through interaction with the physical environment. In our framework, executable Behavior Trees are repeatedly refined by a Large Language Model actor using structured natural-language feedback produced by a Vision-Language Model critic that observes the physical robot and execution traces. Unlike conventional reinforcement learning, policy updates in VRL occur directly at the symbolic planning level, without gradient-based optimization. This enables transparent reasoning, explicit causal feedback, and human-interpretable policy evolution. We validate the proposed framework on a real mobile robot performing a multi-stage manipulation and navigation task under execution uncertainty. Experimental results show that the framework supports explainable policy improvements, closed-loop adaptation to execution failures, and reliable deployment on physical robotic systems.

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