  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.
