N-ACXIS Lab @ UIUC

Our group director, Hiroyasu Tsukamoto, has been appointed as a Visiting Professor at the Korean Advanced Institute of Science and Technology (KAIST).

Our new papers “Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems” and “Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation” are accepted for publication at IEEE CDC.

We welcome Sunidhi Pandey to our group (https://hirotsukamoto.com/people/)! She will be co-advised by Prof. SooJean Han at KAIST.

Rihan Aaron Dsilva won the 2026 Aerospace Engineering Poster Competition for his work “Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems.”

We put together an online lecture series on Statistical Contraction Theory for Learning-based Control of Nonlinear Systems, based on materials used for the DISC Winter School at TU/e (https://hirotsukamoto.com/contraction/).

Our new papers “Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safety” and “Conformal Contraction for Robust Nonlinear Control with Distribution-Free Uncertainty Quantification” are accepted for publication at IEEE CDC.

We offer open postdoctoral researcher positions jointly with Prof. SooJean Han at the Korea Advanced Institute of Science and Technology (https://hirotsukamoto.com/positions/).

Our paper “Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects” has received the Editor’s Choice Award at AIAA JGCD.

Our group director, Hiroyasu Tsukamoto, has been appointed as an Associate Editor for IEEE RA-L in the area of Aerial and Field Robotics & Theoretical Foundations (https://www.ieee-ras.org/publications/ra-l/information-for-authors-ra-l).

Our group director, Hiroyasu Tsukamoto, has been named Forbes 30 Under 30 Asia – Class of 2025 in its Healthcare and Science category. See Forbes Selects Hiroyasu Tsukamoto.

Our work on interstellar object exploration was highlighted on our department’s website. This article also features amazing contributions from our undergraduate researchers, Arna Bhardwaj and Shishir Bhatta, presented at the IEEE Aerospace Conference in Montana.

It’s long overdue, but we’ve created a page to introduce our new N-ACXIS members—check it out!

Our group director, Hiroyasu Tsukamoto, also received the Outstanding Innovator Award from Dentsu Soken for the Innovators Under 35 Award from MIT Technology Review (Region: Japan).

Our group director, Hiroyasu Tsukamoto, received the Innovators Under 35 Award from MIT Technology Review (Region: Japan).

Our new papers “Regret-Optimal Defense Against Stealthy Adversaries: A System Level Approach” and “Robust Optimal Network Topology Switching for Zero Dynamics Attacks” are accepted for publication at IEEE CDC.

Our new paper “Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects” is accepted for publication at AIAA JGCD.

Our tutorial session on “Planning and Control with Machine Learning for Autonomous and Robotic Systems,” by Soon-Jo Chung (Caltech), Hiroyasu Tsukamoto (UIUC), and Guanya Shi (CMU) won the Best Tutorial Award at IEEE SMC.

ACXIS Lab has multiple open Ph.D. and M.S. positions (https://hirotsukamoto.com/positions/) for highly motivated students.

Our new paper “CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems” is accepted for publication at IEEE CDC. This is a joint work with NASA JPL.

We are going to host a tutorial session “Planning and Control with Machine Learning for Autonomous and Robotic Systems” at IEEE SMC. This session gives a tutorial overview of machine learning control systems with safety and stability guarantees.

Hiroyasu Tsukamoto’s doctoral dissertation won the William F. Ballhaus Prize for an outstanding dissertation in Space Engineering, GALCIT, Caltech.

We hosted a tutorial session “Contraction Theory for Machine Learning” at IEEE CDC. This website provides an overview of contraction theory for nonlinear stability analysis and control synthesis, with an emphasis on deriving formal guarantees for learning-based control problems.