Dynamic Role

Dynamic role assignment in multi-agent systems, particularly involving robots and humans, focuses on optimizing task allocation and collaboration by adapting roles based on real-time factors like performance, environmental conditions, and individual capabilities. Current research employs reinforcement learning, particularly hierarchical and multi-agent approaches, often incorporating behavior trees or mixed-integer linear programming for efficient role selection and task planning. This research is significant for improving the efficiency and safety of human-robot collaboration in diverse applications, from manufacturing and logistics to search and rescue, by enabling flexible and adaptive teamwork.