The field of multi-robot systems and autonomous control is rapidly advancing, with a focus on developing innovative frameworks and algorithms for efficient and robust coordination. Recent developments have highlighted the importance of knowledge graphs, reinforcement learning, and morphology-aware approaches in enabling adaptive and explainable autonomous systems. Notably, the integration of graph neural networks and soft actor-critic algorithms has shown promise in improving sample efficiency and robustness in tensegrity robot control. Furthermore, unified memory-based frameworks have emerged as a key solution for achieving lifelong adaptability, scalable coordination, and robust scheduling in multi-agent systems.

Some noteworthy papers in this area include: Policies over Poses, which proposes a scalable and outlier-robust distributed pose-graph optimization framework using multi-agent reinforcement learning. Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion, which introduces a framework that integrates a graph neural network into the soft actor-critic algorithm for improved locomotion control. RoboOS-NeXT, which presents a unified memory-based framework for lifelong, scalable, and robust multi-robot collaboration.