Manipulability Maximization

Manipulability maximization focuses on optimizing robot dexterity and control, aiming to enhance performance in tasks ranging from surgery to bin-picking. Current research emphasizes developing algorithms and models, such as particle filters and quadratic programming, to achieve this optimization while respecting constraints like joint limits, collision avoidance, and actuator capabilities, often within a Riemannian geometry framework to handle inherent geometric constraints of the data. This research is crucial for improving the efficiency, safety, and robustness of robots in various applications, particularly in human-robot collaboration and minimally invasive surgery.