Model-Based AI planning and Execution Systems for Robotics

TL;DR

This paper reviews the development of model-based planning and execution systems in robotics, highlighting recent advances, design choices, and future directions for integrating these systems with modern robotic platforms.

Contribution

It provides a comprehensive overview of existing model-based robotic planning systems, analyzing their design choices and proposing future research directions.

Findings

Recent systems like ROSPlan demonstrate integration with modern platforms.

Diverse design choices impact system flexibility and performance.

Future development avenues include improved reasoning architectures.

Abstract

Model-based planning and execution systems offer a principled approach to building flexible autonomous robots that can perform diverse tasks by automatically combining a host of basic skills. This idea is almost as old as modern robotics. Yet, while diverse general-purpose reasoning architectures have been proposed since, general-purpose systems that are integrated with modern robotic platforms have emerged only recently, starting with the influential ROSPlan system. Since then, a growing number of model-based systems for robot task-level control have emerged. In this paper, we consider the diverse design choices and issues existing systems attempt to address, the different solutions proposed so far, and suggest avenues for future development.