通过融合谐波控制李雅普诺夫-障碍函数与不安全的以物体为中心的动作策略实现在线安全控制
Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies
AI总结：
该研究提出融合信号时序逻辑导出的谐波控制李雅普诺夫-障碍函数与任意机器人策略的方法，通过安全证书将不安全策略转化为有形式化保证的安全策略，在机械臂避障任务中验证了有效性且可推广到复杂场景。
摘要：
我们提出一种方法，将从信号时序逻辑（STL）规范导出的谐波控制李雅普诺夫-障碍函数（HCLBFs）与任意给定的机器人策略相结合，将不安全策略转化为具有形式化保证的安全策略。这两个组件通过HCLBF导出的安全证书进行融合，从而生成既保留安全性又保留任务驱动行为的指令。我们通过一个简单的概念验证实现进行演示，该实现针对以物体为中心的基于力的策略（通过强化学习训练，用于固定机械臂的移动任务），结合安全约束后能够避免与桌面上的障碍物碰撞。所提方法可推广到更复杂的规范和动态任务场景。
英文摘要：
We propose a method for combining Harmonic Control Lyapunov-Barrier Functions (HCLBFs) derived from Signal Temporal Logic (STL) specifications with any given robot policy to turn an unsafe policy into a safe one with formal guarantees. The two components are combined via HCLBF-derived safety certificates, thus producing commands that preserve both safety and task-driven behavior. We demonstrate with a simple proof-of-concept implementation for an object-centric force-based policy trained through reinforcement learning for a movement task of a stationary robot arm that is able to avoid colliding with obstacles on a table top after combining the policy with the safety constraints. The proposed method can be generalized to more complex specifications and dynamic task settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
