  We present a task-and-motion planning (TAMP) algorithm robust against a human
operator's cooperative or adversarial interventions. Interventions often
invalidate the current plan and require replanning on the fly. Replanning can
be computationally expensive and often interrupts seamless task execution. We
introduce a dynamically reconfigurable planning methodology with behavior
tree-based control strategies toward reactive TAMP, which takes the advantage
of previous plans and incremental graph search during temporal logic-based
reactive synthesis. Our algorithm also shows efficient recovery functionalities
that minimize the number of replanning steps. Finally, our algorithm produces a
robust, efficient, and complete TAMP solution. Our experimental results show
the algorithm results in superior manipulation performance in both simulated
and real-world tasks.
