Abdullah Altwaitan

Research Interests

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Abdullah Altwaitan

Assistant Professor at Kuwait University

Office
COEP S02E2-236
Email
abdullah.altwaitan@ku.edu.kw

I am an Assistant Professor in the Department of Electrical Engineering at Kuwait University. I received my Ph.D. in Electrical Engineering (Intelligent Systems, Robotics, and Control) from the University of California San Diego in 2026, where I worked with Prof. Nikolay Atanasov. My dissertation focused on Online Model Learning and Control for Autonomous Robot Navigation. Prior to that, I worked as a Project Engineer at Honeywell. I received my B.S. and M.S. degrees in Electrical Engineering from Arizona State University in 2017 and 2019, respectively.

Updates

  1. Jul’26
    Our paper, Adapting Neural Robot Dynamics on the Fly for Predictive Control, has been accepted to the 65th IEEE Conference on Decision and Control (CDC).
  2. Oct’25
    Our paper, Port‑Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control, has received the 2025 Best Paper Award from the IEEE RAS Technical Committee on Robot Control.
  3. Sep’25
    Our paper, Learned IMU Bias Prediction for Invariant Visual Inertial Odometry, has been accepted to IEEE Robotics and Automation Letters (RA‑L).
  4. May’25
    Our paper, Port‑Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control, has received an Honorable Mention for the 2024 IEEE Transactions on Robotics (T‑RO) King‑Sun Fu Best Paper Award.
  5. Nov’24
    Our paper, Variable‑Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots, has been accepted to IEEE Robotics and Automation Letters (RA‑L).
  6. Jun’24
    Our paper, Port‑Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control, has been accepted to IEEE Transactions on Robotics (T‑RO).
  7. Jan’24
    Our paper, Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control, has been accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA).

Research

  1. Quadrotor flying a figure-eight trajectory in the lab Quadrotor performing a flip

    Adapting Neural Robot Dynamics on the Fly for Predictive Control

    Abdullah Altawaitan, Nikolay Atanasov

    IEEE Conference on Decision and Control (CDC), 2026.

  2. Onboard camera view with tracked visual features Estimated trajectory and landmarks in 3D

    Learned IMU Bias Prediction for Invariant Visual Inertial Odometry

    Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal, Thai Duong, Nikolay Atanasov

    IEEE Robotics and Automation Letters (RA‑L), 2025.

  3. Quadruped robot jumping onto a box Quadruped robot performing consecutive jumps

    Variable‑Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots

    Chuong Nguyen, Abdullah Altawaitan, Thai Duong, Nikolay Atanasov, Quan Nguyen

    IEEE Robotics and Automation Letters (RA‑L), 2025.

  4. Quadrotor flying outdoors during data collection Quadrotor tracking a trajectory around obstacles

    Port‑Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control

    Thai Duong, Abdullah Altawaitan, Jason Stanley, Nikolay Atanasov

    IEEE Transactions on Robotics (T‑RO), 2024.

    • Best Paper Award — 2025 IEEE RAS TC on Robot Control
    • Honorable Mention — 2024 IEEE T‑RO King‑Sun Fu Best Paper Award
  5. LiDAR point cloud around a mobile robot Jackal ground robot driving outdoors

    Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control

    Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal, Thai Duong, Nikolay Atanasov

    IEEE International Conference on Robotics and Automation (ICRA), 2024.

Teaching

BibTeX Citation