Max Robotics — Global Service Robot Directory + US Certification

About

Armand Jordana is a rising force in robotics, specializing in the intersection of model-predictive control (MPC), optimization, and agile locomotion. His research focuses on enabling robots to perform complex, dynamic maneuvers in highly constrained environments, such as navigating stepping stones or avoiding collisions in real time. Jordana’s major contributions include pioneering diffusion-based learning for contact planning, which marries model-based control with search and learning to achieve unprecedented agility in legged robots. He has also advanced MPC by introducing hard collision avoidance constraints for manipulators and developing structure-exploiting sequential quadratic programming solvers that rival differential dynamic programming in efficiency. His work on risk-sensitive estimation and infinite-horizon value function approximation addresses critical challenges in robustness and stability under uncertainty. With over 30 citations across his most-cited papers since 2022, including a 2024 paper on agile locomotion with 7 citations, Jordana’s impact is already notable. His 2025 paper on structure-exploiting SQP for MPC underscores his push to rethink solver efficiency. For students and researchers, Jordana’s work exemplifies how blending control theory, optimization, and learning can push the boundaries of autonomous robotics.