Mixed Autonomy

Mixed autonomy research focuses on developing systems where humans and autonomous agents share control, aiming to leverage the strengths of both for improved performance and safety in complex tasks. Current research emphasizes robust methods for dynamically balancing autonomy levels based on context and operator capabilities, often employing reinforcement learning, control theory, and symbolic reasoning within multi-agent frameworks. This field is crucial for advancing robotics, autonomous driving, and human-computer interaction, with applications ranging from assistive technologies to large-scale traffic management and collaborative multi-robot systems.