Description
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
Table of Contents
1. Introduction2. Neural Networks and Backpropagation3. Convolutional Neural Networks4. Graph Convolutional Networks5. Recurrent Neural Networks6. Deep Reinforcement Learning7. Lightweight Deep Learning8. Knowledge Distillation9. Progressive and Compressive Deep Learning10. Representation Learning and Retrieval11. Object Detection and Tracking12. Semantic Scene Segmentation for Robotics13. 3D Object Detection and Tracking14. Human Activity Recognition15. Deep Learning for Vision-based Navigation in Autonomous Drone Racing16. Robotic Grasping in Agile Production17. Deep learning in Multiagent Systems18. Simulation Environments19. Biosignal time-series analysis20. Medical Image Analysis21. Deep learning for robotics examples using OpenDR
