Robotic arms are increasingly deployed in uncertain environments, yet conventional control pipelines often become rigid and brittle when exposed to perturbations or incomplete information. Virtual Model Control (VMC) enables compliant behaviors by embedding virtual forces and mapping them into joint torques, but its reliance on fixed parameters and limited coordination among virtual components constrains adaptability and may undermine stability as task objectives evolve. To address these limitations, we propose Adaptive VMC with Large Language Model (LLM)- and Lyapunov-Based Reinforcement Learning (RL), which preserves the physical interpretability of VMC while supporting stability-guaranteed online adaptation. The LLM provides structured priors and high-level reasoning that enhance coordination among virtual components, improve sample efficiency, and facilitate flexible adjustment to varying task requirements. Complementarily, Lyapunov-based RL enforces theoretical stability constraints, ensuring safe and reliable adaptation under uncertainty. Extensive simulations on a 7-DoF Panda arm demonstrate that our approach effectively balances competing objectives in dynamic tasks, achieving superior performance while highlighting the synergistic benefits of LLM guidance and Lyapunov-constrained adaptation.

翻译：机械臂在不确定环境中的部署日益增多，然而传统控制流程在面临扰动或信息不完整时往往变得僵化且脆弱。虚拟模型控制通过嵌入虚拟力并将其映射为关节扭矩来实现顺应行为，但其对固定参数的依赖以及虚拟组件间有限的协调性限制了适应性，并可能随着任务目标演变而破坏稳定性。为克服这些局限，我们提出基于大语言模型与李雅普诺夫的强化学习自适应虚拟模型控制，该方法在保持VMC物理可解释性的同时支持稳定性保障的在线适应。LLM提供结构化先验与高层推理，以增强虚拟组件间的协调性、提升样本效率，并促进对不同任务需求的灵活调整。与之互补的是，基于李雅普诺夫的强化学习强制执行理论稳定性约束，确保在不确定性下的安全可靠适应。在7自由度Panda机械臂上的大量仿真表明，我们的方法能有效平衡动态任务中的竞争目标，在实现卓越性能的同时，凸显了LLM引导与李雅普诺夫约束适应的协同优势。