I'm currently a Ph.D. student at Stanford's Autonomous Systems Lab, advised by Prof. Marco Pavone. My research focuses on creating algorithms for safe and efficient learning + control of complex systems.

Prior to Stanford, I earned a B.S. in Mechanical Engineering with a minor in Aerospace from the California Institute of Technology. At Caltech, among other projects, I delved into learning of residual dynamics with Prof. Soon-Jo Chung at the Autonomous Robotics and Control Lab, and worked on aerial manipulation under Prof. Joel Burdick. I founded Caltech Air and Outer Space (CAOS) and held leadership roles in teams that secured over $360,000 in the 2021 and 2022 NASA BIG Idea Challenge. I have also interned at both Honeywell Aerospace and the NASA Jet Propulsion Laboratory.

   /      /      /   lpabon [at] stanford (dot) edu

profile photo


How can robots efficiently learn from their own experiences, safely interact with the world around them, and adapt to unfamiliar environments?

Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer (Π-MPC)
Luis A. Pabon, Johannes Köhler, John Alora, Patrick Benito Eberhard, Andrea Carron, Melanie Zeilinger, Marco Pavone
International Conference on Intelligent Robots and Systems (IROS), 2024

Even when our model of a system is imperfect, we can use a simple periodic observer to achieve perfect tracking for periodic trajectories.

Robust nonlinear reduced order model predictive control Robust nonlinear reduced order model predictive control
Robust Nonlinear Reduced-Order Model Predictive Control
John Alora, Luis A. Pabon, Johannes Köhler, Mattia Cenedese, Edward Schmerling, Melanie Zeilinger, George Haller, Marco Pavone
Conference on Decision and Control (CDC), 2023

We quantify the uncertainty introduced after reducing the dimensionality of a system model and leverage it in a predictive control approach, ensuring stable and safe operation by dynamically adjusting how cautious the controller is.

What will the future of Lunar and Martian exploration look like?

Before: EDS panel full of dust After: clean EDS panel
Design of a Modular and Orientable Electrodynamic Shield for Lunar Dust Mitigation

Panels that tile together and generate a traveling electric field to automatically clean dust from any surface - the first modular implementation of electrodynamic dust shielding (EDS).

( denotes equal contribution)

Project Highlights

  • LATTICE (Lunar Architecture for Tree-Traversal In-service-of Cabled Exploration)

    Founder and Autonomy lead, advised by Prof. Soon-Jo Chung (full credits in tech. paper)

  • LATTICE Shuttle

    Featured in National Geographic, NASA, and Caltech [1], [2] news

    Watch the full LATTICE System demonstration

  • Axel Rover
    Tether Management System

    Internship @ NASA JPL Robotic Mobility Group under Dr. Travis Brown and Dr. Issa Nesnas

  • HOMES (Habitat Orientable & Modular Electrodynamic Shield)

    Founder and Manufacturing lead, advised by Prof. Soon-Jo Chung (full credits in tech. paper)
    Featured in NASA and Caltech news

  • Tensile Analysis of HOMES Materials

    Comprehensive testing to determine material properties for design and modeling, advised by Prof. Michael Mello

  • Hill-Climber

    For Caltech's ME72 Engineering Design Competition (2021)

Selected Honors

Teaching & Service