Manual Control

Manual control research focuses on improving human-machine interaction, particularly in assistive robotics and prosthetics, aiming to create intuitive and effective interfaces that seamlessly blend human volition with automated assistance. Current research emphasizes hybrid control architectures, combining user intent with intelligent algorithms (e.g., impedance controllers, preemptive stabilization mechanisms) to enhance performance and safety, often incorporating machine learning for predictive modeling of user actions and adaptive control strategies. These advancements hold significant promise for improving the functionality and usability of assistive devices, expanding capabilities for individuals with motor impairments and enhancing human-robot collaboration in various applications.