Robot Perception And Learning
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Deep Learning for Robot Perception and Cognition
Author: Alexandros Iosifidis
language: en
Publisher: Academic Press
Release Date: 2022-02-04
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
Robot Perception and Learning
This book is divided into five chapters. Chapter 1 introduces the background of the research, the content positioning, and some related open source resources. Chapter 2 discusses some benchmarking issues related to the field of embodied intelligence and mobile robotics. Chapter 3 introduces robot perception, especially the object detection and tracking based on 3D lidar with contemporary characteristics. Chapter 4 introduces robot learning, especially robot online learning methods with strong embodied intelligence features. Chapter 5 summarizes the book and provides prospects for future research and application directions. Reading this book helps readers have a systematic understanding of the latest research in related fields. The book mainly introduces methods and principles, and the corresponding experimental results need to refer to the corresponding scientific papers. This book is aimed at practitioners in the field with a certain knowledge base, including but not limited to graduate students, Ph.D. students, postdocs, etc.
Machine Learning and Robot Perception
Author: Bruno Apolloni
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-09-14
This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.