Autonomous Control

Autonomous control research aims to develop systems capable of independently executing complex tasks, prioritizing safety and efficiency. Current efforts focus on improving robustness and reliability through techniques like deep reinforcement learning (including variations such as Soft Actor-Critic and D3QN), model predictive control, and the integration of large language models for higher-level decision-making and risk assessment. This field is crucial for advancing various applications, from safer vehicles and improved robotic systems to more efficient resource management in areas like agriculture and space exploration.