Admittance Control

Admittance control is a robotic control strategy that allows robots to respond compliantly to external forces, enabling safe and intuitive interaction with humans and environments. Current research focuses on improving admittance control's performance in various applications, including human-robot collaboration (HRC), minimally invasive surgery, and rehabilitation robotics, often employing adaptive control algorithms, iterative learning, and deep learning for subtask recognition and parameter tuning. These advancements are significant for enhancing safety and efficiency in HRC, improving surgical precision, and creating more effective rehabilitation tools.