Alejandro Astudillo
Papers
9
Total Citations
55
H-Index
4
About
Alejandro Astudillo is a robotics and control researcher whose work sits at the intersection of nonlinear model predictive control (NMPC), optimal control, and robot manipulation. His research focuses primarily on developing computationally efficient, real-time applicable control frameworks for robot manipulators, with a particular emphasis on making advanced control techniques practical and accessible. Among his most significant contributions is the development of tunnel-following NMPC schemes, which allow robot manipulators to exploit permissible deviations around a reference path — work that has garnered 21 citations and established him as a notable voice in constrained robot motion planning. He has further advanced the field by investigating hybrid differentiation strategies — combining algorithmic and analytical derivatives — to accelerate optimal control problem solutions, and by leveraging task and data parallelism for computational speed-ups. Astudillo also demonstrates a strong commitment to open-source tooling: his Tasho Python toolbox and an accompanying web-based graphical interface lower the barrier for researchers and engineers to prototype OCP-based robot motion skills. His more recent work extends into flexible object manipulation and novel optimization algorithms, such as Anderson Accelerated Feasible Sequential Linear Programming. Collectively, his publications reflect a researcher dedicated to bridging theoretical rigor with deployable, real-world robotic solutions.
Research Focus
Key Achievements
Top Papers
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- 7Towards an Open Toolchain for Fast Nonlinear MPC for Serial Robots2 citations · 2020
- 8Anderson Accelerated Feasible Sequential Linear Programming2 citations · 2023
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