{"title":"The role of machine learning in robotics","authors":"R. Bogue","doi":"10.1108/ir-11-2022-0279","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis paper aims to illustrate the growing role of machine learning techniques in robotics.\n\n\nDesign/methodology/approach\nFollowing an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides examples of robotic machine learning applications in agriculture, waste management, warehouse automation and exoskeletons. This is followed by a short consideration of applications in future generations of self-driving vehicles. Finally, brief conclusions are drawn.\n\n\nFindings\nMachine learning is a branch of artificial intelligence and the topic of extensive academic study. Recent years have seen machine learning techniques being applied successfully to a diversity of robotic systems, most of which involve machine vision. They have imparted these with a range of unique or greatly improved operational capabilities, allowing them to satisfy all manner of new applications.\n\n\nOriginality/value\nThis provides a detailed insight into how machine learning is being applied to robotics.\n","PeriodicalId":54987,"journal":{"name":"Industrial Robot-The International Journal of Robotics Research and Application","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Robot-The International Journal of Robotics Research and Application","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1108/ir-11-2022-0279","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 1
Abstract
Purpose
This paper aims to illustrate the growing role of machine learning techniques in robotics.
Design/methodology/approach
Following an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides examples of robotic machine learning applications in agriculture, waste management, warehouse automation and exoskeletons. This is followed by a short consideration of applications in future generations of self-driving vehicles. Finally, brief conclusions are drawn.
Findings
Machine learning is a branch of artificial intelligence and the topic of extensive academic study. Recent years have seen machine learning techniques being applied successfully to a diversity of robotic systems, most of which involve machine vision. They have imparted these with a range of unique or greatly improved operational capabilities, allowing them to satisfy all manner of new applications.
Originality/value
This provides a detailed insight into how machine learning is being applied to robotics.
期刊介绍:
Industrial Robot publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of robotic technology, and reflecting the most interesting and strategically important research and development activities from around the world.
The journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations. Industrial Robot''s coverage includes, but is not restricted to:
Automatic assembly
Flexible manufacturing
Programming optimisation
Simulation and offline programming
Service robots
Autonomous robots
Swarm intelligence
Humanoid robots
Prosthetics and exoskeletons
Machine intelligence
Military robots
Underwater and aerial robots
Cooperative robots
Flexible grippers and tactile sensing
Robot vision
Teleoperation
Mobile robots
Search and rescue robots
Robot welding
Collision avoidance
Robotic machining
Surgical robots
Call for Papers 2020
AI for Autonomous Unmanned Systems
Agricultural Robot
Brain-Computer Interfaces for Human-Robot Interaction
Cooperative Robots
Robots for Environmental Monitoring
Rehabilitation Robots
Wearable Robotics/Exoskeletons.