IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS
Other text in scientific journal, 2021

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human–robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has increased since the appearance of recent machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as graphics processing units (GPUs), has made numerous robotic applications feasible which were not possible previously.

Author

Aysegul Ucar

Firat University

Jessy W. Grizzle

University of Michigan

Maani Ghaffari

University of Michigan

Mattias Wahde

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

H. Levent Akin

Bogazici University

Jacky Baltes

National Taiwan Normal University

H. Isil Bozma

Bogazici University

Jaime Valls Miro

University of Technology Sydney

IEEE Access

2169-3536 (ISSN)

Vol. 9 89694-89698

Subject Categories

Robotics

Computer Systems

DOI

10.1109/ACCESS.2021.3090135

More information

Latest update

8/16/2021