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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Neural networks and backpropagation - p. 17 3. Chapter 3. Convolutional neural networks - p. 35 4. Chapter 4. Graph convolutional networks - p. 71 5. Chapter 5. Recurrent neural networks - p. 101 6. Chapter 6. Deep reinforcement learning - p. 117 7. Chapter 7. Lightweight deep learning - p. 131 8. Chapter 8. Knowledge distillation - p. 165 9. Chapter 9. Progressive and compressive learning - p. 187 10. Chapter 10. Representation learning and retrieval - p. 221 11. Chapter 11. Object detection and tracking - p. 243 12. Chapter 12. Semantic scene segmentation for robotics - p. 279 13. Chapter 13. 3D object detection and tracking - p. 313 14. Chapter 14. Human activity recognition - p. 341 15. Chapter 15. Deep learning for vision-based navigation in autonomous drone racing - p. 371 16. Chapter 16. Robotic grasping in agile production - p. 407 17. Chapter 17. Deep learning in multiagent systems - p. 435 18. Chapter 18. Simulation environments - p. 461 19. Chapter 19. Biosignal time-series analysis - p. 491 20. Chapter 20. Medical image analysis - p. 541 21. Chapter 21. Deep learning for robotics examples using OpenDR - p. 579 22. Index - p. 597
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.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Neural networks and backpropagation - p. 17 3. Chapter 3. Convolutional neural networks - p. 35 4. Chapter 4. Graph convolutional networks - p. 71 5. Chapter 5. Recurrent neural networks - p. 101 6. Chapter 6. Deep reinforcement learning - p. 117 7. Chapter 7. Lightweight deep learning - p. 131 8. Chapter 8. Knowledge distillation - p. 165 9. Chapter 9. Progressive and compressive learning - p. 187 10. Chapter 10. Representation learning and retrieval - p. 221 11. Chapter 11. Object detection and tracking - p. 243 12. Chapter 12. Semantic scene segmentation for robotics - p. 279 13. Chapter 13. 3D object detection and tracking - p. 313 14. Chapter 14. Human activity recognition - p. 341 15. Chapter 15. Deep learning for vision-based navigation in autonomous drone racing - p. 371 16. Chapter 16. Robotic grasping in agile production - p. 407 17. Chapter 17. Deep learning in multiagent systems - p. 435 18. Chapter 18. Simulation environments - p. 461 19. Chapter 19. Biosignal time-series analysis - p. 491 20. Chapter 20. Medical image analysis - p. 541 21. Chapter 21. Deep learning for robotics examples using OpenDR - p. 579 22. Index - p. 597
