Deep learning for robot perception and cognition

• Titel: Deep learning for robot perception and cognition
• Verf.angabe: edited by Alexandros Iosifidis, Anastasios Tefas
• Verlagsort: [Place of publication not identified]
• Verlag: Academic Press
• Jahr: 2022
• Umfang: 1 online resource
• ISBN: 978-0-323-88572-0
• ISBN: 0-323-88572-1
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Robotique
• Sach-SW: Vision artificielle (Robotique)
• Sach-SW: Robotique ; Facteurs humains
• Sach-SW: Robot vision
• Sach-SW: Robotics
• Sach-SW: Robotics ; Human factors
• K10plus-PPN: 1892763923
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4544473837
• Lokale URL Inst.: Zum Volltext

Abstract

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis.