用安全约束让不安全机器人策略在线变安全，有理论保证。
Achieving Safe Control Online through Integration of Harmonic Control Lyapunov-Barrier Functions with Unsafe Object-Centric Action Policies
- 用HCLBF生成安全证书，融合任意机器人策略
- 实测可避免机械臂碰撞桌面上障碍物
- 适合需安全保证的强化学习控制场景
我们提出一种方法，将基于信号时序逻辑（STL）规范生成的谐振控制李雅普诺夫-屏障函数（HCLBFs）与任意给定的机器人策略结合，使不安全策略在在线控制中获得形式化安全保障。通过HCLBF生成的安全证书，协调动作指令以同时保持安全性与任务驱动行为。我们在一个概念验证实验中，对一个基于强化学习训练的对象中心力控策略进行测试，该策略针对静止机械臂的运动任务，在加入安全约束后成功避免了桌面障碍物碰撞。该方法可推广至更复杂的规范和动态任务场景。
原文摘要 · Abstract (English)
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.
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