# Task Decomposition for Iterative Learning Model Predictive Control

## TL;DR

This paper introduces a task decomposition approach for iterative learning model predictive control, enabling the reuse of data from one task to efficiently solve related subsequent tasks in nonlinear systems.

## Contribution

It provides a formal framework for subtask definition and demonstrates feasibility and cost improvement in iterative control for complex tasks.

## Key findings

- Feasibility of the proposed task decomposition method.
- Cost improvement over initializations.
- Successful application in autonomous racing and robotic manipulation.

## Abstract

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.