Machine Learning Techniques for Assistive Robotics

ISBN 10: 3039363387 / ISBN 13: 9783039363384

Published by MDPI AG, 2020

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.

Seller Inventory # 9783039363384

Bibliographic Details

• Title: Machine Learning Techniques for Assistive Robotics
• Publisher: MDPI AG
• Publication Date: 2020
• Language: English
• ISBN 10: 3039363387
• ISBN 13: 9783039363384
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 18D millimeters 250 millimeters width by 175 millimeters height by 18 millimeters depth
• Weight: 725 grams

About this title

Synopsis

Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.