Department of Mechatronics Engineering, Firat University, Elâzığ, Turkey
Jessy W. Grizzle
Department of Electrical Engineering and Computer Science Robotics, Institute University of Michigan, Ann Arbor, MI, USA
Maani Ghaffari
Department of Naval Architecture and Marine Engineering Robotics, Institute University of Michigan, Ann Arbor, MI, USA
Mattias Wahde
Department of Mechanics and Maritime Sciences, Division of Vehicle Engineering and Autonomous Systems, Chalmers University of Technology, Gothenburg, Swedend
H. Levent Akin
Department of Computer Engineering, Boğaziçi University, Istanbul, Turkey
Jacky Baltes
Department of Electrical Engineering, Educational Robotics Centre, National Taiwan Normal University, Taipei, Taiwan
H. Isil Bozma
Department of Electrical and Electronic Engineering, Boğaziçi University, Istanbul, Turkey
Jaime Valls Miro
School of Mechanical and Mechatronic Engineering, Robotics Institute University of Technology Sydney, Sydney, NSW, Australia
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.
