Safe Learning & Trustworthy Autonomy

We develop learning-based control and planning frameworks that enable autonomous systems to operate safely and reliably in uncertain, dynamic environments. Our research integrates reinforcement learning, safety-critical control, uncertainty quantification, and risk-aware decision-making to provide trustworthy autonomy with formal safety guarantees.

  • Risk-certified, event-triggered replanning for navigation among moving obstacles
  • Control barrier function safety filters for mobile robots and multi-robot teams
  • Learning-based disturbance estimation with provable robustness
  • Stochastic safety guarantees for systems that rely on unreliable communication
A legged robot and a wheeled robot move along a busy park path among pedestrians and cyclists, each person wrapped in a translucent safety zone, with drones overhead and a city skyline at sunset.

Representative publications

Lab members are in bold. ★ PhD student, ♦ Master's student, † equal contribution. The full list is on the Publications page.

2026

  1. CoRL
    Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation
    Richie R. Suganda★ and Bin Hu
    In Proceedings of the 2026 Conference on Robot Learning (Acceptance Rate: 32.8%) (CoRL), 2026
  2. Figure from Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
    IEEE RAL
    Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control
    Fan Zhang, Richie R. Suganda★, Jinfeng Chen, Wenhua Liu, Hantao Fu, Bin Hu, and Qin Lin
    IEEE Robotics and Automation Letters (IEEE RAL), 2026
  3. IROS
    Formation-Aware Adaptive Conformalized Perception for Safe Leader-Follower Multi-Robot Systems
    Richie R. Suganda★ and Bin Hu
    In Proceedings of the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (Acceptance Rate: 36%) (IROS), Sep 2026
  4. Figure from Unified disturbance-aware safe kinematic control for closed-architecture robots
    MECH
    Unified disturbance-aware safe kinematic control for closed-architecture robots
    Fan Zhang, Jinfeng Chen, Joseph J. B. Mvogo Ahanda, Hanz Richter, Ge Lv, Bin Hu, and Qin Lin
    Mechatronics (MECH), 2026

2025

  1. ICRA
    Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods
    Richie R Suganda★, Tony Tran♦, Miao Pan, Lei Fan, Qin Lin, and Bin Hu
    In Proc. IEEE Int. Conf. on Robotics and Automation (Acceptance Rate: 38.67%) (ICRA), 2025

2024

  1. IEEE CSL
    Quadrotor Fault-Tolerant Control at High Speed: A Model-Based Extended State Observer for Mismatched Disturbance Rejection Approach
    Jinfeng Chen, Fan Zhang, Bin Hu, and Qin Lin
    IEEE Control Systems Letters (IEEE CSL), 2024

2021

  1. ACC
    On a notion of stochastic zeroing barrier function
    Tua A Tamba, Bin Hu, and Yul Y Nazaruddin
    In 2021 American Control Conference (ACC), 2021

2019

  1. AUTOM
    Co-design of safe and efficient networked control systems in factory automation with state-dependent wireless fading channels
    Bin Hu, Yebin Wang, Philip V Orlik, Toshiaki Koike-Akino, and Jianlin Guo
    Automatica (AUTOM), 2019

2015

  1. IEEE TAC
    Distributed switching control to achieve almost sure safety for leader-follower vehicular networked systems
    Bin Hu and Michael D Lemmon
    IEEE Transactions on Automatic Control (IEEE TAC), 2015