Advances in Computational Learning for Robotics – a PeerJ Collection in collaboration with RiTA2020
The theme for the 8th International Conference on Robot Intelligence Technology and Applications (RiTA) is “Ready, willing and able for Robot Intelligence Tech & Apps?” RiTA 2020 aims to serve as a platform for academics, researchers, government & policymakers, experts, industrial practitioners and other relevant stakeholders in the dissemination of new knowledge on RiTA fundamental research, design & developing RiTA, working & living with RiTA, and shaping RiTA for ethical and legal governance.
PeerJ will sponsor a PeerJ Award for the best PhD 3 Minute Thesis presentation at the conference, as selected by the organising committee. The award winner will have the opportunity to be featured on the PeerJ blog to discuss their research and will receive a free publication in PeerJ Computer Science (subject to peer review).
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 aim of this PeerJ Computer Science 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. It 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 piezoelectric actuators
How to submit
Submission to the collection is open now, with a deadline of March 31st 2021.
If you would like your research to be considered for this Collection, please first send your manuscript title and abstract to Dr Liu (email@example.com). 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). Authors should review the submission instructions for PeerJ Computer Science before submitting.
Authors should note that, if accepted for publication, they will be required to pay either an Article Processing Charge ($1195) or for a PeerJ Lifetime Membership (a one-off payment for lifetime publishing privileges – starting at just $399).
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