PeerJ is excited to work with PeerJ Computer Science Academic Editor Dr Pengcheng Liu and RiTA 2020 to publish a Collection on Advances in Computational Learning for Robotics.
The aim of this Collection is to highlight the roles of advanced computational learning approaches for robotics applications and prior knowledge in achieving the successes and, especially, how they contribute to the taming of the complexity of the linked domains.
PeerJ Computer Science is currently accepting submissions for this Collection, with a submission deadline of 31st March 2021.
If you would like your research to be considered for this Collection, please first send your manuscript title and abstract to Dr Liu (firstname.lastname@example.org). If considered in scope, you will be invited to submit to PeerJ Computer Science and your manuscript will be handled by the Collection Editors for this call (subject to availability).
Advances in Computational Learning for Robotics
Robotics is a multidisciplinary research field with enormous potential, concerning the development of intelligent robotic systems that are capable of making decisions and acting autonomously in real and unpredictable environments to accomplish tasks and assist humans with relevant and beneficial results for society. Recently, advances in the computational study of the intelligent behaviors such as learning and adaptation have led to powerful insights about the nature of learning in both humans, animals, materials and machines. However, new and challenging theoretical and technological problems are being posed. One can apply the computational metaphor in different ways, and computational learning has become an important topic within many paradigms, including artificial intelligence, pattern recognition, control theory, cognitive intelligence, behavioral intelligence and statistics. Such convergence of interests is encouraging, but few researchers in this active area communicate across disciplinary boundaries, and even fewer are skilled in the ‘language’ and techniques of more than one approach. With this new era of computational learning for robotics, much research is needed in order to continue to advance the field and also to evaluate the multidisciplinary concerns of the existence learning and adaptation techniques.
The scope of the collection includes, but is not limited to, the following topics:
• Behavioural and biological learning and control
• Cognitive computation
• Robot localization, mapping, exploration, and navigation
• Evolutionary robotics, multi-robot systems and swarm intelligence
• Human-robot interaction and collaboration
• The Internet of Robotic Things
• Smart materials
• Soft robotics
• Robot motion planning and grasping
• Multimodal perception and sensor fusion
• Bio-inspired approaches for robot design, control and optimization
• Morphological computation and embodied intelligence
• Imitation learning, Bayesian/probabilistic learning
• Micro and Nano robotics
• Multi-agent systems
• Humanoid robotics
• Biomechatronics and rehabilitation
• Self-adaptation and learning
• Flexible electronics and piezoelectret actuators
Dr Pengcheng Liu, University of York, United Kingdom
Professor Hyun Myung, KAIST, South Korea
Dr Roger Yu Dong, Curtin University, Australia
Professor Guibin Bian, Chinese Academy of Sciences, China
Dr Junwen Zhong, University of California Berkeley, United States
Dr Esyin Chew, EUREKA Robotics Lab, Cardiff Metropolitan University, United Kingdom