| Title: |
Closed-Loop Verbal Reinforcement Learning for Task-Level Robotic Planning |
| Authors: |
Plotnikov, Dmitrii; Kolomiets, Iaroslav; Maliukov, Dmitrii; Kosenkov, Dmitrij; Zinniatullina, Daniia; Trandofilov, Artem; Gazaryan, Georgii; Bogatikov, Kirill; Kozlov, Timofei; Duchinskii, Igor; Konenkov, Mikhail; Cabrera, Miguel Altamirano; Tsetserukou, Dzmitry |
| Publication Year: |
2026 |
| Collection: |
ArXiv.org (Cornell University Library) |
| Subject Terms: |
Robotics |
| Description: |
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. |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
http://arxiv.org/abs/2603.22169 |
| Availability: |
http://arxiv.org/abs/2603.22169 |
| Accession Number: |
edsbas.6B3B8013 |
| Database: |
BASE |