Zachary Manchester

Zachary Manchester is an Associate Professor in the Robotics Institute at Carnegie Mellon University, where he leads research on control, optimization, and physics-based modeling for highly dynamic robotic systems. His work focuses on fast trajectory optimization, contact-implicit control, and real-time model predictive control for robots operating under significant physical constraints. Manchester and his collaborators have developed influential algorithms and tools—including ALTRO for constrained trajectory optimization, contact-implicit MPC frameworks, and differentiable physics engines such as Dojo—that push the boundaries of scalable, real-world robot control. His research spans ground robots, aerial vehicles, and space systems, including the pioneering KickSat project demonstrating ultra-small spacecraft. He works at the intersection of numerical optimization, robotics, and dynamics, with the broader goal of making autonomous systems more agile, reliable, and physically grounded. Manchester’s work has been featured in top venues such as ICRA, IROS, RA-L, and the International Symposium on Robotics Research.