Precision in Motion: Real-Time Control for Dexterous Robotic Hands

Controlling human-like robotic hands isn't just hard—it's a high-dimensional, contact-rich challenge. This research brings motion control to the forefront with sampling-based model predictive control (MPC), enabling a tendon-driven biomimetic hand to manipulate objects in real time. By combining a physics-based simulator with a visual-language model that refines task objectives on the fly, the system achieves complex in-hand manipulation—like flipping and catching a ball—with no lengthy training required. The result? Adaptive, precise motion control that mimics human dexterity with machine efficiency.