February 24th, 2021 
Daniela Lopez de Luise Research Topic: Learning, Perception, and Collaboration for Robots in Industrial Environments
Journal: Frontiers in Robotics and AI
Automation and robotics in modern factories are constantly evolving.
Collaborative robots represent one of the major changes in industrial
robotics and their market is undergoing strong growth. A collaborative
robot is designed for direct interaction with a human to improve the
work experience and to reduce the risk of injuries. Among the most
important challenges in the development of collaborative robots are
those related to performing tasks in unstructured environments, moving
in a shared workspace, manipulating objects, and learning from expert
operators. In order to achieve such goals, collaborative robots should
take advantage of recent innovations in machine learning, like deep
learning, as well as of advances in algorithms for robot perception and
new sensor technologies.
The goal of this Research Topic is to investigate new methods for
intelligent collaborative robots in industrial environments. We are also
looking forward to works exploring new approaches that have been
evaluated in other field robotics domains, like healthcare environments.
Authors are also encouraged to submit papers that discuss new research
findings that have been evaluated in laboratory experiments. A
particular focus of the Research Topic will be the development of novel
algorithms for robot perception, task planning, human-robot interaction,
programming by demonstration, and safe deep reinforcement learning. Both
theoretical contributions and application validations are welcome with
review articles also encouraged.
This Research Topic welcomes contributions on topics including, but not
limited to, the following:
• Human-robot interaction and collaboration in industrial environments
• Deep Learning for Visual Perception
• Learning from Demonstration
• Learning in Grasping and Manipulation
• RGB-D Perception
• Range Sensing
• Industrial robots and Industry 4.0
• Automated guided vehicles (AGVs) and laser-guided vehicles (LGVs)
• Sensor-based planning
• Active vision
• Computer Vision for Automation
• Computer Vision for Manufacturing
• Semantic Scene Understanding
• Object manipulation and intelligent path planning
• Safe Reinforcement Learning
*** Important Dates ***
Deadline for full manuscripts: 22 June 2021
For more information or to indicate your interest in submitting, please
visit our Research Topic Page
Sincerely,
Prof. Jacopo Aleotti, University of Parma
Dr. Riccardo Monica, University of Parma
Dr. Matteo Saveriano, University of Innsbruck
Topic Editors
Frontiers in Robotics and AI
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