IEEE Access Special Section Editorial: Real-Time Machine Learning Applications in Mobile Robotics

Aysegul Ucar,Jaime Valls Miro,H Levent Akin,Mattias Wahde,Jessy W Grizzle,Jacky Baltes,Maani Ghaffari,H Isil Bozma

doi:10.1109/access.2021.3090135

Abstract

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.

Highlights

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
There is a need for new algorithms and more explainable and interpretable models that receive and process data from the sensors such as cameras, light detection and ranging (LIDAR), inertial measurement unit (IMU), and global positioning system (GPS), preferably in an unsupervised or semi-supervised fashion. This IEEE ACCESS Special Section on Real-Time Machine Learning Applications in Mobile Robotics aims to present works related to the design and usage of recent machine learning methods for robotics applications, focusing on the state-of-the-art methods, such as deep learning, generative adversarial networks, scalable evolutionary algorithms, reinforcement learning, probabilistic graphical models, Bayesian methods, and explainable and interpretable approaches

Summary

Introduction

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS This IEEE ACCESS Special Section on Real-Time Machine Learning Applications in Mobile Robotics aims to present works related to the design and usage of recent machine learning methods for robotics applications, focusing on the state-of-the-art methods, such as deep learning, generative adversarial networks, scalable evolutionary algorithms, reinforcement learning, probabilistic graphical models, Bayesian methods, and explainable and interpretable approaches.

Results

Conclusion