Brain-Inspired Control and Learning for Robotics and Automation

Note to contributors

The link on the official website can be found here. Please check here for the PDF version. Please do include the special session code q4tw2 when submitting your manuscript to PaperPlaza.

Goal

Today, there has been tremendous need for robot and automation in industry. Since most of the robots work in a well-structured environment, attention has been omitted to enhance the generalizability of such robot systems. However, with the rise of individualized, customized, and small-batch manufacture, there are increasing requirements for the adaptability of the robot systems in semi-structured/reconfigured-structured environments. Due to the human’s nature of handling complex and changing problems, the research of brain-inspired robotics is no doubt a considerable solution to such challenging robot and automation scenarios.

This special session aims to discuss the recent brain-inspired control and learning methods for robotics and automation, thereby providing a comprehensive overview for future potential directions in both theoretical and engineering aspects. Authors are encouraged to submit research papers that present their state-of-the-art research and applications in all the related area of brain-inspired control and learning methods for robotics and automation.

Session Title

Brain-Inspired Control and Learning for Robotics and Automation

Organizers

• Dr. Rui Li, Hongshen Faculty

  School of Automation, Chongqing University

  E-mail: rui.li[AT]cqu.edu.cn

• Dr. Zhenshan Bing, Research Associate

  Technical University of Munich

  E-mail: bing[AT]in.tum.de

• Dr. Jiahao Chen, Assistant Professor

  Institute of Automation, Chinese Academy of Sciences

  E-mail: jiahao.chen[AT]ia.ac.cn