Complex Robotic System

Complex robotic systems research aims to develop robust, efficient, and safe robots capable of performing intricate tasks in dynamic environments. Current efforts focus on improving control algorithms (e.g., Model Predictive Control enhanced by transformers, Koopman operators), developing efficient motion planning techniques (leveraging deep learning and geometric/topological methods), and addressing safety and ethical considerations through constraint-based learning and intelligent disobedience frameworks. These advancements are crucial for deploying robots in real-world applications, ranging from industrial automation and elderly care to challenging locomotion scenarios, while also contributing to fundamental understanding of robot learning and control.