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About

Alejandro Castro is a leading researcher in robotic contact modeling and control, whose work is transforming how robots physically interact with their environments. His core contributions lie in developing mathematically rigorous, convex formulations for frictional contact—a notoriously difficult problem in robotics. Castro’s breakthrough “Convex Formulation of Compliant Contact” (2022, 25 citations) provides a provably convergent solver that eliminates contact constraints, enabling robust and efficient simulation. He extended this framework to handle coupled rigid and deformable bodies (2023, 7 citations), and his “Irrotational Contact Fields” (2025) offers a general method for generating convex approximations of complex contact models. On the planning and control side, Castro pioneered “Inverse Dynamics Trajectory Optimization for Contact-Implicit Model Predictive Control” (2023/2025, 13+ citations), achieving real-time contact-implicit MPC through a surprisingly simple and effective method. Earlier, his work on fast model-based pose estimation for highly deformable tactile sensors (2019, 33 citations) addressed the challenge of large deformations in soft robotics. With over 80 total citations, Castro’s research provides the theoretical foundations and practical algorithms that are enabling robots to reliably make, break, and maintain contact during complex manipulation and locomotion tasks.