Deep Learning for Robot Perception and Cognition - Tapa blanda

Acerca de los autores

Alexandros Iosifidis is a Professor at Aarhus University, Denmark. He leads the Machine Learning and
Computational Intelligence group at the Department of Electrical and Computer Engineering. He received his Ph.D.
from the Department of Informatics at Aristotle University of Thessaloniki, Greece in 2014. He participated in more
than 15 research and development projects financed by national and European funds.

Anastasios Tefas received the B.Sc. in Informatics in 1997 and the Ph.D. degree in Informatics in 2002, both from
the Aristotle University of Thessaloniki, Greece. Since 2017, he has been an Associate Professor at the Department of
Informatics, Aristotle University of Thessaloniki. Dr. Tefas participated in 20 research projects financed by national and
European funds. He is the coordinator of the H2020 project OpenDR, “Open Deep Learning Toolkit for Robotics.”

De la contraportada

Traditional robotic systems rely on extensive feature engineering for sensor data and environment state representation, followed by data analysis steps that are usually disconnected from the data representation. Deep Learning-based methodologies, however, combine all these steps in an end-to-end learning based process which is optimized through experience, enabling the identification and exploitation of complex patterns in sensory data, leading to higher performance levels and robust solutions in real-time.

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. It 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.

Deep Learning for Robot Perception and Cognition is a textbook and reference suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics and Deep Learning focusing on Robotic perception and cognition tasks.