Robots interacting with humans must be safe, reactive and adapt online to unforeseen environmental and task changes. Achieving these requirements concurrently is a challenge as interactive planners lack formal safety guarantees, while safe motion planners lack flexibility to adapt. To tackle this, we propose a modular control architecture that generates both safe and reactive motion plans for human-robot interaction by integrating temporal logic-based discrete task level plans with continuous Dynamical System (DS)-based motion plans. We formulate a reactive temporal logic formula that enables users to define task specifications through structured language, and propose a planning algorithm at the task level that generates a sequence of desired robot behaviors while being adaptive to environmental changes. At the motion level, we incorporate control Lyapunov functions and control barrier functions to compute stable and safe continuous motion plans for two types of robot behaviors: (i) complex, possibly periodic motions given by autonomous DS and (ii) time-critical tasks specified by Signal Temporal Logic~(STL). Our methodology is demonstrated on the Franka robot arm performing wiping tasks on a whiteboard and a mannequin that is compliant to human interactions and adaptive to environmental changes.

翻译：与人类交互的机器人必须确保安全、具备反应性，并能在线适应不可预见的环境变化与任务变更。同时满足这些需求具有挑战性，因为交互式规划器缺乏形式化安全保障，而安全运动规划器又缺乏适应灵活性。为解决这一问题，我们提出了一种模块化控制架构，通过将基于时态逻辑的离散任务层规划与基于连续动力学系统（DS）的运动规划相结合，生成既安全又具有反应性的人机交互运动方案。我们构建了反应式时态逻辑公式，使用户能够通过结构化语言定义任务规范，并提出了一种任务层规划算法，该算法能生成预期机器人行为序列，同时适应环境变化。在运动层，我们结合控制李雅普诺夫函数与控制障碍函数，为两类机器人行为计算稳定安全的连续运动方案：(i) 由自主动力学系统生成的复杂（可能周期性）运动；(ii) 由信号时态逻辑（STL）指定的时间关键型任务。该方法在Franka机械臂上进行了验证，该机械臂在白板和人体模型上执行擦拭任务时，既能符合人类交互需求，又能适应环境变化。