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Shuo Liu

I am a Ph. D candidate at Boston University Robotics Laboratory (since Sep. 2021) advised by Prof. Calin A. Belta. I received my Master’s degree in Mechanical Engineering from Columbia University advised by Prof. Richard W. Longman.

My research interest includes safety-critical control theory and trustworthy machine learning with applications to robotics and multi-agent systems. I welcome collaborations on related topics. If you are interested, please reach out by email.

Email: liushuo [at] bu (dot) edu [Google Scholar] / [Github] / [LinkedIn]


    A global Control Lyapunov Function (CLF) is a scalar function used to design controllers that stabilize a nonlinear system globally (i.e., from any initial state) while a global Control Barrier Function (CBF) is a function used to ensure that a system's state remains within a safe set (e.g., avoids obstacles) for all time. CBFs are famous recently since they: 1. Ensure forward invariance of safe sets, guaranteeing system safety; 2. Allow real-time implementation via optimization-based controllers; 3. Can be combined with CLFs to balance safety and stability; 4. Work well for nonlinear and high-dimensional systems. 5. Provide a modular framework for handling multiple safety constraints.

    My ambition is to ensure the safety of complex systems using CBFs, either through analytical methods or a combination of analytical and machine learning approaches, without being overly conservative. I aim to apply this to fields such as autonomous driving and robotics.

    Research highlights

  •   Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions, IEEE-ACC 2025: ArXiv
  •    Auxiliary-Variable Adaptive Control Barrier Functions, submitted to IEEE-TAC: Arxiv
  •   Control Barrier Functions via Minkowski Operations for Safe Navigation among Polytopic Sets, IEEE-CDC 2025: ArXiv

Recent News (last update: July. 2025)

  • [07/2025] Two papers are accepted in IEEE-CDC 2025.
  • [01/2025] One paper is accepted in IEEE-ACC 2025.
  • [07/2024] Two papers are accepted in IEEE-CDC 2024.
  • [07/2023] One paper is accepted in IEEE-CDC 2023.
  • [01/2023] One paper is accepted in IEEE-ACC 2023.


    Selected Journal Papers

    Auxiliary-Variable Adaptive Control Barrier Functions
    Shuo Liu, Wei Xiao, and Calin A. Belta
    submitted to IEEE Transactions on Automatic Control (TAC)
    ArXiv , Bibtex
    Learning-Enabled Iterative Convex Optimization for Safety-Critical Model Predictive Control
    Shuo Liu, Zhe Huang, Jun Zeng, Koushil Sreenath and Calin A. Belta
    submitted to IEEE Open Journal of Control Systems (OJ-CSYS)
    ArXiv, Bibtex


    Selected Conference Papers

    Control Barrier Functions via Minkowski Operations for Safe Navigation among Polytopic Sets
    Yi-Hsuan Chen, Shuo Liu, Wei Xiao, Calin A. Belta and Michael Otte
    64th IEEE Conference on Decision and Control (CDC), 2025
    ArXiv, Bibtex
    Ensuring Safe and Smooth Control in Safety-Critical Systems via Filtered Control Barrier Functions
    Shuo Liu, Wei Xiao and Calin A. Belta
    submitted to IEEE-ACC 2026
    ArXiv, Bibtex
    Risk-Aware Adaptive Control Barrier Functions for Safe Control of Nonlinear Systems under Stochastic Uncertainty
    Shuo Liu and Calin A. Belta
    64th IEEE Conference on Decision and Control (CDC), 2025
    ArXiv, Bibtex
    Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions
    Shuo Liu*, Yihui Mao* and Calin A. Belta
    IEEE American Control Conference (ACC), 2025
    ArXiv, Bibtex
    Auxiliary-Variable Adaptive Control Lyapunov Barrier Functions for Spatio-Temporally Constrained Safety-Critical Applications
    Shuo Liu, Wei Xiao and Calin A. Belta
    63rd IEEE Conference on Decision and Control (CDC), 2024
    ArXiv, Publisher, Bibtex
    Feasibility-Guaranteed Safety-Critical Control with Applications to Heterogeneous Platoons
    Shuo Liu, Wei Xiao and Calin A. Belta
    63rd IEEE Conference on Decision and Control (CDC), 2024
    ArXiv, Publisher, Bibtex
    Auxiliary-Variable Adaptive Control Barrier Functions for Safety Critical Systems
    Shuo Liu, Wei Xiao and Calin A. Belta
    62nd IEEE Conference on Decision and Control (CDC), 2023
    ArXiv, Publisher, Bibtex
    Iterative Convex Optimization for Model Predictive Control with Discrete-Time High-Order Control Barrier Functions
    Shuo Liu*, Jun Zeng*, Koushil Sreenath and Calin A. Belta
    IEEE American Control Conference (ACC), 2023
    ArXiv, Publisher, Code, Bibtex
    On Designing Finite Time Iterative Learning Control Based on Steady State Frequency Response
    Shuo Liu, Richard W. Longman and Benjamas Panomruttanarug
    AAS/AIAA Astrodynamics Specialist Conference, 2021
    ArXiv, Publisher, Bibtex
    Modifying and Optimizing the Inverse of the Frequency Response Circulant Matrix as an Iterative Learning Control Compensator
    Shuo Liu and Richard W. Longman
    31st AAS/AIAA Space Flight Mechanics Meeting, 2021
    ArXiv, Publisher, Bibtex


    Arxiv Papers

    A-OctoMap: An Adaptive OctoMap for Online Path Planning
    Yihui Mao and Shuo Liu
    ArXiv Preprint, 2024
    ArXiv, Bibtex
    Mathematical Optimization of Resolution Improvement in Structured Light Data by Periodic Scanning Motion: Application for Feedback during Lunar Landing
    Tarek A. Elsharhawy, P. James Schuck, Shuo Liu and Luc Saikali
    ArXiv Preprint, 2024
    ArXiv, Bibtex
    A Method to Speed Up Convergence of Iterative Learning Control for High Precision Repetitive Motions
    Richard W. Longman, Shuo Liu and Tarek A. Elsharhawy
    ArXiv Preprint, 2023
    ArXiv, Bibtex