Navigation remains a cornerstone challenge for mobile robots, encompassing a wide range of systems, from unmanned vehicles and aerial drones to maritime vessels and autonomous delivery robots. Reinforcement Learning (RL) has emerged as a powerful tool to address these challenges, leveraging its ability to learn from experience and optimize decision-making processes. By enabling agents to learn how to act in a way that maximizes rewards based on their interactions with the environment, RL offers unique advantages for dynamic and adaptive navigation. This ability to continuously improve through online exploration and feedback makes RL particularly well-suited for complex and unpredictable real-world scenarios.
As RL techniques advance, there is a growing trend towards their application in real-world robot navigation tasks. This Research Topic seeks to provide a comprehensive overview of the current state-of-the-art in RL for robot navigation, highlighting the latest research, methodologies, and practical implementations. We aim to address the theoretical foundations, algorithmic innovations, and experimental validations that contribute to the field. By showcasing cutting-edge advancements and fostering dialogue among experts, we hope to drive forward the development of RL-based navigation systems and enhance their integration into practical applications.
We encourage contributions that explore a variety of aspects, including but not limited to: the development of novel RL algorithms tailored for real-world navigation challenges, integration with sensor technologies, robustness and adaptability in diverse environments, and comparative analyses with traditional navigation methods. This Research Topic is designed to facilitate cross-disciplinary collaboration, bringing together researchers from computational intelligence, control systems, robotics, and related fields to advance the frontier of RL-driven navigation solutions.
This Research Topic aims to provide a comprehensive overview of the latest research and advancements in applying reinforcement learning techniques to real-world robot navigation.
We invite high-quality contributions that cover, but are not limited to:
• Deep reinforcement learning approaches (e.g., robot control and motion planning using DRL)
• Multi-agent reinforcement learning (e.g., cooperative, competitive, self-play, etc)
• Navigation in unknown environments (e.g., autonomous flying through trees, jungles)
• Multi-robot collaboration (e.g., UAV, UGV, USV cooperation)
• Collaborative autonomous driving (e.g., collaborative perception, decision-making, and planning)
• Hierarchical reinforcement learning (e.g., skill discovery, hierarchical representations)
• Reinforcement learning algorithms (e.g., new algorithms for existing settings and new settings)
• Reinforcement learning based decision-making (e.g., autonomous driving)
• Uncertainty-aware motion planning (e.g., manipulation)
• Robot exploration (e.g., curiosity-driven learning, multi-robot exploration)
• Applied reinforcement learning (e.g., operations)
• Real-world reinforcement learning systems (e.g., distributed training, multi-GPU training)
• Multi-task reinforcement learning (e.g., task allocation)
• Goal-based skill learning (e.g., motion control)
Keywords:
Navigation, Reinforcement Learning, Robot, Real-World, Deployment
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.
Navigation remains a cornerstone challenge for mobile robots, encompassing a wide range of systems, from unmanned vehicles and aerial drones to maritime vessels and autonomous delivery robots. Reinforcement Learning (RL) has emerged as a powerful tool to address these challenges, leveraging its ability to learn from experience and optimize decision-making processes. By enabling agents to learn how to act in a way that maximizes rewards based on their interactions with the environment, RL offers unique advantages for dynamic and adaptive navigation. This ability to continuously improve through online exploration and feedback makes RL particularly well-suited for complex and unpredictable real-world scenarios.
As RL techniques advance, there is a growing trend towards their application in real-world robot navigation tasks. This Research Topic seeks to provide a comprehensive overview of the current state-of-the-art in RL for robot navigation, highlighting the latest research, methodologies, and practical implementations. We aim to address the theoretical foundations, algorithmic innovations, and experimental validations that contribute to the field. By showcasing cutting-edge advancements and fostering dialogue among experts, we hope to drive forward the development of RL-based navigation systems and enhance their integration into practical applications.
We encourage contributions that explore a variety of aspects, including but not limited to: the development of novel RL algorithms tailored for real-world navigation challenges, integration with sensor technologies, robustness and adaptability in diverse environments, and comparative analyses with traditional navigation methods. This Research Topic is designed to facilitate cross-disciplinary collaboration, bringing together researchers from computational intelligence, control systems, robotics, and related fields to advance the frontier of RL-driven navigation solutions.
This Research Topic aims to provide a comprehensive overview of the latest research and advancements in applying reinforcement learning techniques to real-world robot navigation.
We invite high-quality contributions that cover, but are not limited to:
• Deep reinforcement learning approaches (e.g., robot control and motion planning using DRL)
• Multi-agent reinforcement learning (e.g., cooperative, competitive, self-play, etc)
• Navigation in unknown environments (e.g., autonomous flying through trees, jungles)
• Multi-robot collaboration (e.g., UAV, UGV, USV cooperation)
• Collaborative autonomous driving (e.g., collaborative perception, decision-making, and planning)
• Hierarchical reinforcement learning (e.g., skill discovery, hierarchical representations)
• Reinforcement learning algorithms (e.g., new algorithms for existing settings and new settings)
• Reinforcement learning based decision-making (e.g., autonomous driving)
• Uncertainty-aware motion planning (e.g., manipulation)
• Robot exploration (e.g., curiosity-driven learning, multi-robot exploration)
• Applied reinforcement learning (e.g., operations)
• Real-world reinforcement learning systems (e.g., distributed training, multi-GPU training)
• Multi-task reinforcement learning (e.g., task allocation)
• Goal-based skill learning (e.g., motion control)
Keywords:
Navigation, Reinforcement Learning, Robot, Real-World, Deployment
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.