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 ways that maximize 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.

In parallel with the rise of RL, Foundation Models—large-scale pre-trained models that can be fine-tuned across a variety of tasks—are transforming the landscape of AI. Foundation models, such as large language models and vision transformers, have demonstrated the capacity to generalize across multiple domains and tasks. Their ability to handle multimodal data and adapt to diverse contexts opens new possibilities for enhancing robotic systems. In robotics, combining reinforcement learning with foundation models presents exciting opportunities to create more robust, adaptable, and scalable systems, particularly in environments where agents must reason over vast amounts of sensory data or engage in complex decision-making.

This Research Topic seeks to provide a comprehensive overview of the current state-of-the-art in Reinforcement Learning and Foundation Models for robot navigation and control, highlighting the latest research, methodologies, and practical implementations. We aim to address the theoretical foundations, algorithmic innovations, and experimental validations that contribute to these fields. By showcasing cutting-edge advancements and fostering dialogue among experts, we hope to drive forward the development of RL- and foundation model-based systems, enhancing their integration into practical robotics applications.

We encourage contributions that explore a variety of aspects, including but not limited to:
- Novel RL algorithms tailored for real-world navigation challenges, including integrations with sensor technologies, and the robustness and adaptability of RL in diverse environments.
- The role of foundation models in robotics, including how pre-trained models can be fine-tuned for specific robotic tasks such as navigation, manipulation, and multi-agent collaboration.
- Comparative analyses between RL and foundation models, focusing on their strengths and limitations in autonomous systems.
- Multimodal learning using foundation models, integrating visual, auditory, and sensor data for enhanced decision-making.
- Interdisciplinary research on combining RL with foundation models, including transfer learning, domain adaptation, and continual learning.
- Practical challenges and innovations in the deployment of RL and foundation model-driven systems in real-world robotics.
- Cross-disciplinary collaborations that bring together researchers from computational intelligence, control systems, robotics, and related fields to advance the frontier of intelligent navigation solutions.

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 forests or complex terrains).
- 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 and novel 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).
- Foundation models for robotics (e.g., pre-trained vision or language models for robotic manipulation, multi-modal learning).
- Transfer learning and fine-tuning large models for specific robotic tasks.
- Multi-modal reasoning and decision-making (e.g., integrating vision, audio, sensor data for robust navigation).

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Frontiers in Robotics and AI

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