Dual Control

Dual control is a framework for decision-making under uncertainty, particularly in interactive systems where an agent must predict and influence the behavior of others. Current research focuses on applying dual control to complex scenarios like autonomous driving and human-robot interaction, often employing model predictive control and Bayesian inference within sampling-based algorithms to balance exploration (learning about others) and exploitation (optimizing own actions). These advancements aim to improve the safety and efficiency of autonomous systems by enabling more robust and adaptable interactions with unpredictable environments and human partners. The resulting algorithms are being tested in both simulation and real-world settings, demonstrating potential for significant impact across various fields.