Deep Learning for Robot Perception and Cognition, First Edition
- 12h 47m
- Alexandros Iosifidis, Anastasios Tefas
- Elsevier Science and Technology Books, Inc.
- 2022
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
About the Author
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.”
In this Book
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Introduction
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Neural Networks and Backpropagation
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Convolutional Neural Networks
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Graph Convolutional Networks
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Recurrent Neural Networks
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Deep Reinforcement Learning
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Lightweight Deep Learning
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Knowledge Distillation
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Progressive and Compressive Learning
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Representation Learning and Retrieval
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Object Detection and Tracking
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Semantic Scene Segmentation for Robotics
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3D Object Detection and Tracking
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Human Activity Recognition
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Deep Learning for Vision-Based Navigation in Autonomous Drone Racing
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Robotic Grasping in Agile Production
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Deep Learning in Multiagent Systems
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Simulation Environments
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Biosignal Time-Series Analysis
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Medical Image Analysis
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Deep Learning for Robotics Examples Using OpenDR
