The integration of neural network-based intelligent algorithms with robotics has revolutionized the field of robotics in recent years. Inspired by the human brain, neural networks have shown great potential in enabling robots to learn from data, make intelligent decisions, and perform complex tasks. These algorithms have been applied in various areas of robotics, including perception, control, planning, and learning. More precisely, convolutional neural networks (CNNs) have significantly improved robot vision capabilities, while recurrent neural networks (RNNs) have enhanced sequential data processing for tasks such as speech recognition and natural language understanding. Deep reinforcement learning algorithms have enabled robots to learn optimal control policies through interaction with their environment. Additionally, re-current neural networks have contributed to the stability control, performance improvement, and redundancy resolution of robots. The continuous advancements in neural network-based algorithms in robotics holds great promise for the future of intelligent robotic systems.
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.
The integration of neural network-based intelligent algorithms with robotics has revolutionized the field of robotics in recent years. Inspired by the human brain, neural networks have shown great potential in enabling robots to learn from data, make intelligent decisions, and perform complex tasks. These algorithms have been applied in various areas of robotics, including perception, control, planning, and learning. More precisely, convolutional neural networks (CNNs) have significantly improved robot vision capabilities, while recurrent neural networks (RNNs) have enhanced sequential data processing for tasks such as speech recognition and natural language understanding. Deep reinforcement learning algorithms have enabled robots to learn optimal control policies through interaction with their environment. Additionally, re-current neural networks have contributed to the stability control, performance improvement, and redundancy resolution of robots. The continuous advancements in neural network-based algorithms in robotics holds great promise for the future of intelligent robotic systems.
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.