About this Research Topic
The field of neural network-based intelligent algorithms in robotics is rapidly evolving, but there are still challenges that need to be addressed. This Research Topic aims to address these challenges and find solutions to further advance the practical applications of these algorithms in robotics. Specifically, we seek to address issues such as the stability and efficiency of neural network models in real-time robotic systems, the interpretability of their decision-making processes, and the robustness and adaptability of these algorithms to handle uncertainties and dynamic environments, and the practical applications of these algorithms in robotics. To achieve this, we encourage researchers to investigate and propose novel network architectures, optimization methods, and training techniques that can overcome these challenges. Recent advances, such as the integration of recurrent neural networks, deep reinforcement learning, imitation learning, meta-learning approaches, and transfer learning techniques, will be explored to enhance the capabilities and performance of neural network-based intelligent algorithms in robotics. In addition, we encourage researchers to investigate applications on various types of robots, including redundant manipulators, swarm robots, unmanned aerial vehicles, and soft robots.
Topics of contributing papers include, but are not limited to:
- Innovative design of neural networks and control algorithms for convergence, robustness, and other characteristics.
- Neural network architectures and performance improvement for robotic systems.
- Robotic task execution and control based on reinforcement learning.
- Neural-network-based model predictive control.
- Neural network-based algorithms for robot vision, object recognition, robot navigation or path planning.
- Neural network-based algorithms for robot learning from demonstration
- Adaptive control and stability analysis of neural network-based robotic systems
- Online learning and adaptation for robust robotic algorithms
- Intelligent control of various robotic devices including swarm robots, unmanned aerial vehicle, redundant manipulator, etc.
Keywords: Neural network, reinforcement learning, manipulator, swarm robots, unmanned aerial vehicle, autonomous systems, adaptive control, human-robot interaction, theoretical innovation, model uncertainties, convergence and robustness of algorithms
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.