Introduction

Alexandros Iosifidis, Anastasios Tefas

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

Abstract

Almost everything that we hear about Artificial Intelligence (AI) today is thanks to Machine Learning (ML) and especially the ML algorithms that use neural networks as baseline inference models. This scientific field is called Deep Learning (DL). The core of deep learning is to design, train and deploy end-to-end trainable models that are able to use raw sensor information, build an internal representation of the environment, and perform inference based on this representation. Although this end-to-end training approach has been successfully followed for many different tasks ranging from speech recognition to computer vision and machine translation in the last decade, the big challenge for the next years is to successfully apply the same end-to-end training and deployment approach for robotics, which means to build models that are able to sense and act using a unified deep learning architecture. This chapter provides an introduction to real world problems representation under a deep learning perspective, basic machine learning tasks, shallow and deep learning methodologies, and challenges in adopting deep learning in robotics. Moreover, it provides an introduction to the topics of deep learning for robot perception and cognition covered in the book.

Original languageEnglish
Title of host publicationDeep Learning for Robot Perception and Cognition
EditorsAlexandros Iosifidis, Anastasios Tefas
Number of pages16
PublisherElsevier
Publication date2022
Chapter1
ISBN (Print)9780323885720
ISBN (Electronic)9780323857871
DOIs
Publication statusPublished - 2022

Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Representation learning
  • Robotic cognition
  • Robotic perception
  • Robotics

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