Joint Task

Joint task research explores how multiple agents, including humans and robots, can collaboratively achieve a shared goal, focusing on efficient coordination and communication strategies. Current work investigates optimizing this coordination through various approaches, such as reinforcement learning with causal discovery for resource allocation, and leveraging rate-distortion theory to analyze the impact of communication constraints on task accuracy in semantic communication. These advancements are significant for improving human-robot collaboration, enhancing wireless network efficiency (e.g., in 6G), and developing more robust and natural AI systems capable of interacting effectively in complex environments.