The field of multi-robot systems and autonomous navigation is rapidly advancing, with a focus on developing innovative control strategies and algorithms to ensure safe and efficient operation in complex environments. Recent research has emphasized the importance of resilience, adaptability, and risk-awareness in multi-robot systems, with a particular emphasis on addressing challenges such as misbehaving agents, localization uncertainty, and dynamic obstacles. Notable developments include the use of control barrier functions, belief control barrier functions, and risk-adaptive approaches to ensure safety and avoid collisions in uncertain environments. Additionally, there is a growing interest in distributed optimal control methods, including graph neural network-based approaches, to enable efficient and adaptive control of multi-robot systems. Overall, these advancements have the potential to significantly improve the performance and reliability of multi-robot systems and autonomous navigation in a wide range of applications. Noteworthy papers include: Distributed Resilience-Aware Control in Multi-Robot Networks, which proposes a novel control law for resilient consensus in multi-robot systems, and Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions, which introduces a risk-adaptive approach to safe navigation in dynamic environments.