About this Research Topic
Creating robots with the ability to think, perceive, learn, remember, reason, and interact like human beings can be achieved by taking inspiration from biological control systems. Nowadays, there is a wide variety of robots, ranging from rigid industrial robot arms to soft animal-like robots, that respectively operate in structured and static environments or in unstructured and dynamically-changing environments. Brain-inspired control architectures, mechanisms, and underlying principles can enable robots to operate in challenging scenarios as well as improve conventional robotic designs and control. However, their potential has not been widely implemented in such scenarios.
This Research Topic seeks to explore the current progress in developing biomimetic control architectures that combine biologically based approaches (AI, neural networks) for the next generation of robotic systems. Precisely, this Research Topic investigates novel control strategies that originate from living organisms to reproduce complex tasks (e.g; sensing, walking, jumping, grasping or even flying) in artificial systems. This includes the study, design, and modelling of bio-inspired computing methods to achieve intelligence, flexibility and adaptation for robotic applications such as manipulation, grasping, and locomotion. Furthermore, we aim to highlight how the field of neurorobotics and computational neuroscience can contribute to the improvement of functionally and biologically complex models. We welcome articles on hybrid control approaches, as well as comparative studies.
This topic will promote cross-disciplinary communication in the robotics and the neuroscience communities. We expect that this topic will serve as a platform to facilitate the communication between neuroscience and robotics researchers, and will inspire the fusion of neural processing and bio-inspired robotics research.
Relevant submissions for this Research Topic include, but are not limited to, the following:
- Bio-inspired learning architectures
- Neural-learning-based adaptive control
- Biomimetics robotics
- Sensory-motor integration mechanisms
- Biological AI
- Benchmarking experiments/simulations on modelling and control strategies
- Intelligent robotic behaviour
- Bio-inspired robot control
- Locomotion and Manipulation Control
Keywords: biomimetic control, neural networks, adaptive learning, modelling and control, bio-inspired control, artificial intelligence
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.