The field of robotics is moving towards more autonomous and collaborative systems, with a focus on multi-robot collaboration, adaptability, and safety. Recent developments have enabled robots to perform complex tasks with increasing precision, but challenges remain in generalization, heterogeneity, and safety, especially in large-scale deployments. To address these limitations, researchers are proposing novel frameworks that integrate human oversight, large language models, and heterogeneous robots to optimize task allocation and execution. These frameworks are demonstrating improved performance, robustness, and adaptability in various scenarios, including disaster response and nuclear inspection. Notable papers in this area include:

• HMCF, which proposes a human-in-the-loop multi-robot collaboration framework powered by large language models, achieving higher task success rates and robust zero-shot generalization.
• Automated Hybrid Reward Scheduling via Large Language Models, which dynamically adjusts the learning intensity of each reward component throughout the policy optimization process, enabling robots to acquire skills in a gradual and structured manner.
• Neural Orchestration for Multi-Agent Systems, which implements a supervised learning approach to select the most appropriate agent for each task, achieving high selection accuracy and outperforming baseline strategies.