  A task decomposition method for iterative learning model predictive control
is presented. We consider a constrained nonlinear dynamical system and assume
the availability of state-input pair datasets which solve a task T1. Our
objective is to find a feasible model predictive control policy for a second
task, T2, using stored data from T1. Our approach applies to tasks T2 which are
composed of subtasks contained in T1. In this paper we propose a formal
definition of subtasks and the task decomposition problem, and provide proofs
of feasibility and iteration cost improvement over simple initializations. We
demonstrate the effectiveness of the proposed method on autonomous racing and
robotic manipulation experiments.
