Real-Time Machine Learning Applications In Mobile Robotics

Submission Deadline:  31 July 2020

Submission Deadline: 31 July 2020

IEEE Access invites manuscript submissions in the area of Real-Time Machine Learning Applications In Mobile Robotics. 

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 improved since the appearance of modern 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), have made numerous robotics applications feasible that were not possible previously.

Despite recent advances, there are still gaps in applying available machine learning methods to real robots. Directly transferring algorithms that work successfully in the simulation to the real robot and self-learning of robots are among the current challenges. Moreover, there is also a need for new real-time 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 Special Section in IEEE Access aims to present the works relating to the design and usage of the real-time machine learning methods on all mobile robots including legged and humanoid platforms, focusing on 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. The Special Section will present original research articles covering the implementations and applications of mobile robots and incorporating up-to-date results, theorems, algorithms, and systems.

The topics of interest include, but are not limited to:

  • Robotic learning by simulations
  • Learning and adaptive algorithms for robotic mobile manipulators and humanoid robots
  • Human-robot interaction, learning from human demonstrations
  • Learning for human-robot collaborative tasks
  • Autonomous grasping and manipulation by using mobile robots
  • Multi-robot systems, networked robots, and robot soccer
  • Control, complex action learning, and predictive learning from sensorimotor information for bio-inspired social robots
  • Autonomous driving, navigation, planning, mapping, localization, collision avoidance, and exploration
  • Robotics and autonomous system design and implementation
  • Nonlinear control and visual servoing in robotic systems
  • Soft robotics
  • Usage of sensors, such as EEG, ECG, and IMU in robotics
  • Usage of explainable machine learning and interpretable artificial intelligence in robotics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility and downloads of articles.


Associate Editor:  Aysegul Ucar, Firat University, Turkey

Guest Editors:

    1. Jessy W. Grizzle, University of Michigan, USA
    2. Maani Ghaffari Jadidi, University of Michigan, USA
    3. Mattias Wahde, Chalmers University of Technology, Sweden
    4. Levent Akin, Bogazici University, Turkey
    5. Jacky Baltes, National Taiwan Normal University, Taiwan
    6. Işıl Bozma, Bogazici University, Turkey
    7. Jaime Valls Miro, University of Technology Sydney, Australia


Relevant IEEE Access Special Sections:

  1. Advances in Machine Learning and Cognitive Computing for Industry Applications
  2. Integrative Computer Vision and Multimedia Analytics
  3. Uncertainty Quantification in Robotic Applications

IEEE Access Editor-in-Chief:
  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: agulucar@firat.edu.tr.