Topic: Learning and Collective Intelligence for Multi-Robot Systems

Multi-robot systems are emerging as an important paradigm for enabling scalable, flexible, and intelligent autonomy in complex and dynamic environments. By allowing multiple robots to perceive, communicate, learn, and make decisions collectively, multi-robot systems can accomplish complex tasks more efficiently and robustly than individual robots. Recent advances in robot learning, multi-agent learning, reinforcement learning, distributed intelligence, and swarm intelligence have created new opportunities for developing autonomous robotic systems capable of adaptive coordination, collaborative decision-making, and collective behaviors.

This Special Topic focuses on Learning and Collective Intelligence for Multi-Robot Systems, with the aim of bringing together the latest advances in robot learning, multi-agent learning, coordination, communication, decision-making, and distributed intelligence for collaborative robotic systems. Particular attention is given to how multiple robots can learn and acquire cooperative behaviors through interaction, share information efficiently, adapt to dynamic environments, and achieve collective capabilities beyond those of individual robots. Both multi-agent learning approaches and other learning-based methodologies for multi-robot systems are welcome.

The Special Topic welcomes original research and review articles covering both fundamental methodologies and real-world applications. Topics of interest include multi-robot learning, multi-agent reinforcement learning, multi-robot coordination, collective and swarm intelligence, distributed decision-making, robot communication, heterogeneous multi-robot collaboration, cooperative perception and navigation, multi-robot planning and control, adaptive and continual learning, human–robot collaboration, and edge-enabled distributed intelligence for autonomous robotic systems.

By bridging learning, collective intelligence, and robotics, this Special Topic aims to advance the development of scalable, robust, adaptive, and generalizable multi-robot systems for complex real-world environments.

Topics include, but are not limited to:

● Multi-Robot Learning & Multi-Agent Learning;

● Reinforcement Learning for Multi-Robot Systems;

● Collective & Swarm Intelligence;

● Multi-Robot Coordination & Collaboration;

● Robot Communication & Distributed Decision-Making;

● Heterogeneous & Adaptive Multi-Robot Systems;

● Cooperative Perception & Distributed Sensing;

● Human–Robot Collaboration;

● Distributed & Edge Intelligence for Multi-Robot Systems;

● Learning-Based Applications in Multi-Robot Systems.