A Survey of Knowledge Representation in Service Robotics

TL;DR

This survey reviews how knowledge is represented, gathered, and used in service robotics, highlighting key techniques, challenges, and principles for effective knowledge management in robot problem-solving.

Contribution

It provides a comprehensive overview of knowledge representation methods in service robotics and discusses principles for designing effective representations, distinguishing them from learning models.

Findings

Knowledge representations differ from machine learning models.

Various tools and techniques have been used for knowledge representation.

Effective design principles are crucial for robot problem-solving.

Abstract

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.