Advancing Neural Network-Based Intelligent Algorithms in Robotics: Challenges, Solutions, and Future Perspectives - Volume II

This Research Topic is a part of the 'Advancing Neural Network-Based Intelligent Algorithms in Robotics: Challenges, Solutions, and Future Perspectives' series. Please see the first volume here

Building upon the success of the first volume, this Research Topic continues to explore the transformative integration of neural network-based intelligent algorithms in robotics. Inspired by the human brain, neural networks have demonstrated exceptional potential in enabling robots to learn from data, make intelligent decisions, and perform complex tasks across diverse applications such as perception, control, planning, and learning. Recent advancements in convolutional neural networks (CNNs) have enhanced robot vision capabilities, recurrent neural networks (RNNs) have improved sequential data processing, and deep reinforcement learning has enabled robots to optimize control policies through environmental interactions. However, challenges such as stability, interpretability, robustness, and adaptability remain critical to advancing practical applications.

This volume 2 topic aims to address these ongoing challenges while exploring new horizons for neural network-based algorithms in robotics. We seek contributions that propose novel neural network architectures, training techniques, and optimization strategies to improve real-time efficiency, decision-making transparency, and robustness in dynamic and uncertain environments. Special attention will be given to emerging approaches like imitation learning, meta-learning, transfer learning, and their integration with neural networks to enhance robotic capabilities.

We also encourage research focusing on diverse robotic platforms, including redundant manipulators, swarm robots, unmanned aerial vehicles, and soft robots, as well as applications that demonstrate practical implementations in complex environments. This collection aims to inspire innovative research, foster collaboration, and provide a platform for cutting-edge advancements in neural network-driven robotics.

Topics of contributing papers include, but are not limited to:
- Novel neural network architectures and control algorithms for convergence, robustness, and scalability.
- Enhancements in performance and efficiency for neural-network-based robotic systems.
- Applications of reinforcement learning in robotic task execution and control.
- Neural network-based model predictive control for robotics.
- Algorithms for robot vision, object recognition, navigation, and path planning.
- Learning from demonstration using neural network-based approaches.
- Adaptive control and stability analysis in neural-network-based robotic systems.
- Online learning and adaptation methods for robust and autonomous robotics.
- Intelligent control of robotic systems, including swarm robots, UAVs, redundant manipulators, and soft robots.

This second volume aims to build on the foundation laid by its predecessor while addressing emerging challenges and opportunities in this rapidly evolving field.