Deep Learning for Robot Perception and Cognition

1st Edition - February 4, 2022
Editors: Alexandros Iosifidis, Anastasios Tefas
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 7 8 7 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 8 5 7 2 - 0

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 method… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Acknowledgements
Editors biographies
Chapter 1: Introduction
Abstract
1.1. Artificial intelligence and machine learning
1.2. Real world problems representation
1.3. Machine learning tasks
1.4. Shallow and deep learning
1.5. Robotics and deep learning
References
Chapter 2: Neural networks and backpropagation
Abstract
2.1. Introduction
2.2. Activation functions
2.3. Cost functions
2.4. Backpropagation
2.5. Optimizers and training
2.6. Overfitting
2.7. Concluding remarks
References
Chapter 3: Convolutional neural networks
Abstract
3.1. Introduction
3.2. Structure of convolutional neural networks
3.3. Training convolutional neural networks
3.4. Conclusions
References
Chapter 4: Graph convolutional networks
Abstract
4.1. Introduction
4.2. Spectral graph convolutional network
4.3. Spatial graph convolutional network
4.4. Graph attention network (GAT)
4.5. Graph convolutional networks for large graphs
4.6. Datasets and libraries
4.7. Conclusion
References
Chapter 5: Recurrent neural networks
Abstract
5.1. Introduction
5.2. Vanilla RNN
5.3. Long-short term memory
5.4. Gated recurrent unit
5.5. Other RNN variants
5.6. Applications
5.7. Concluding remarks
References
Chapter 6: Deep reinforcement learning
Abstract
6.1. Introduction
6.2. Value-based methods
6.3. Policy-based methods
6.4. Concluding remarks
References
Chapter 7: Lightweight deep learning
Abstract
7.1. Introduction
7.2. Lightweight convolutional neural network architectures
7.3. Regularization of lightweight convolutional neural networks
7.4. Bag-of-features for improved representation learning
7.5. Early exits for adaptive inference
7.6. Concluding remarks
References
Chapter 8: Knowledge distillation
Abstract
8.1. Introduction
8.2. Neural network distillation
8.3. Probabilistic knowledge transfer
8.4. Multilayer knowledge distillation
8.5. Teacher training strategies
8.6. Concluding remarks
References
Chapter 9: Progressive and compressive learning
Abstract
9.1. Introduction
9.2. Progressive neural network learning
9.3. Compressive learning
9.4. Conclusions
References
Chapter 10: Representation learning and retrieval
Abstract
10.1. Introduction
10.2. Discriminative and self-supervised autoencoders
10.3. Deep representation learning for content based image retrieval
10.4. Model retraining methods for image retrieval
10.5. Variance preserving supervised representation learning
10.6. Concluding remarks
References
Chapter 11: Object detection and tracking
Abstract
11.1. Object detection
11.2. Object tracking
11.3. Conclusion
References
Chapter 12: Semantic scene segmentation for robotics
Abstract
12.1. Introduction
12.2. Algorithms and architectures for semantic segmentation
12.3. Loss functions for semantic segmentation
12.4. Semantic segmentation using multiple inputs
12.5. Semantic segmentation data sets and benchmarks
12.6. Semantic segmentation metrics
12.7. Conclusion
References
Chapter 13: 3D object detection and tracking
Abstract
13.1. Introduction
13.2. 3D object detection
13.3. 3D object tracking
13.4. Conclusion
References
Chapter 14: Human activity recognition
Abstract
14.1. Introduction
14.2. Trimmed action recognition
14.3. Temporal action localization
14.4. Spatiotemporal action localization
14.5. Data sets for human activity recognition
14.6. Conclusion
References
Chapter 15: Deep learning for vision-based navigation in autonomous drone racing
Abstract
15.1. Introduction
15.2. System decomposition approach in drone racing navigation
15.3. Transfer learning and end-to-end planning
15.4. Useful tools for data collection and training
15.5. Conclusions and future work
References
Chapter 16: Robotic grasping in agile production
Abstract
16.1. Introduction
16.2. Grasping and object manipulation
16.3. Grasp evaluation
16.4. Manipulation benchmarking
16.5. Data sets
16.6. Conclusion
References
Chapter 17: Deep learning in multiagent systems
Abstract
17.1. Introduction
17.2. Setting the scene
17.3. Challenges
17.4. Deep learning in multiagent systems
17.5. Conclusion
References
Chapter 18: Simulation environments
Abstract
18.1. Introduction
18.2. Robotic simulators
18.3. Conclusions
References
Chapter 19: Biosignal time-series analysis
Abstract
19.1. Introduction
19.2. ECG classification and advance warning for arrhythmia
19.3. Early prediction of mortality risk for COVID-19 patients
19.4. Conclusion
References
Chapter 20: Medical image analysis
Abstract
20.1. Introduction
20.2. Early detection of myocardial infarction using echocardiography
20.3. COVID-19 recognition from X-ray images via convolutional sparse support estimator based classifier
20.4. Conclusion
References
Chapter 21: Deep learning for robotics examples using OpenDR
Abstract
21.1. Introduction
21.2. Structure of OpenDR toolkit and application examples
21.3. Cointegration of simulation and training
21.4. Concluding remarks
References
Index

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Alexandros Iosifidis

Anastasios Tefas