Learning, Perception, and Collaboration for Robots in Industrial Environments

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

Topic Editors Jacopo Aleotti and Riccardo Monica are involved in ongoing projects supported by local companies who work in the fields of warehouse automation, food and beverages. All other Topic Editors declare no competing interests with regard to the Research Topic subject.

Keywords: Human-Robot Collaboration, Deep Learning Methods, Robot Perception, Industrial Robots, Motion and Task Planning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.