  Task and Motion Planning has made great progress in solving hard sequential
manipulation problems. However, a gap between such planning formulations and
control methods for reactive execution remains. In this paper we propose a
model predictive control approach dedicated to robustly execute a single
sequence of constraints, which corresponds to a discrete decision sequence of a
TAMP plan. We decompose the overall control problem into three sub-problems
(solving for sequential waypoints, their timing, and a short receding horizon
path) that each is a non-linear program solved online in each MPC cycle. The
resulting control strategy can account for long-term interdependencies of
constraints and reactively plan for a timing-optimal transition through all
constraints. We additionally propose phase backtracking when running
constraints of the current phase cannot be fulfilled, leading to a fluent
re-initiation behavior that is robust to perturbations and interferences by an
experimenter.
