用大模型和稳定性约束让机械臂自适应调整，兼顾灵活与安全
Never Too Rigid to Reach: Adaptive Virtual Model Control with LLM- and Lyapunov-Based Reinforcement Learning
- 结合大模型推理与李雅普诺夫强化学习，实现虚拟模型控制的在线自适应
- 在7自由度机械臂上完成动态任务，性能优于传统方法且保证稳定
- 适合需要高灵活性与安全性保障的机器人控制场景
机械臂在不确定环境中应用日益广泛，但传统控制方法在扰动或信息不全时易变得僵化脆弱。虚拟模型控制（VMC）通过嵌入虚拟力并映射为关节扭矩实现柔顺行为，但其依赖固定参数且虚拟组件间协调有限，导致适应性差，任务目标变化时可能破坏稳定性。为此，本文提出基于大语言模型（LLM）与李雅普诺夫约束强化学习的自适应虚拟模型控制（Adaptive VMC），在保持物理可解释性的基础上支持理论保障下的在线适应。大语言模型提供结构化先验与高层推理能力，增强虚拟组件间的协同，提升样本效率，并灵活响应任务需求变化；李雅普诺夫约束强化学习则确保在不确定性下仍具备理论稳定性。在7自由度Panda机械臂上的大量仿真表明，该方法能有效平衡动态任务中的多重目标，性能显著优于基线方法，充分体现了大模型引导与李雅普诺夫约束协同带来的优势。
原文摘要 · Abstract (English)
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.
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