Hierarchical Control

Hierarchical control aims to decompose complex tasks into simpler sub-tasks managed by separate controllers, improving efficiency and robustness. Current research emphasizes learning-based approaches, particularly deep reinforcement learning and model predictive control, often integrated within multi-layered architectures inspired by biological systems or leveraging latent code representations to bridge high-level planning and low-level execution. This framework is proving valuable in diverse applications, including robotics (locomotion, manipulation, multi-robot coordination), autonomous systems, and energy management, offering improved performance and adaptability compared to monolithic control strategies.