Human–AI Collaboration & Multi-Agent Autonomy

We investigate intelligent collaboration between humans and autonomous agents, as well as coordination among interconnected robotic systems. Our research focuses on human-autonomy teaming, trust-aware decision-making, adaptive task allocation, cooperative perception, and distributed control to enable effective and resilient multi-agent operations.

  • Human trust in automation and how automation communicates its reliability
  • Adaptive task allocation for human-machine teams, from manufacturing to m:N operations of uncrewed aircraft
  • Perception-aware distributed formation control for teams of drones and ground robots
  • Resilient vehicle platoons and networked control over unreliable wireless links
An operator wearing a mixed-reality headset directs a wheeled robot, a legged robot, and a drone through a holographic map and task panels, while a construction worker coordinates with the team nearby.

Representative publications

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

2026

  1. 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

2025

  1. Figure from Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs
    IEEE THMS
    Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs
    Katherine R Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, and Bin Hu
    IEEE Transactions on Human-Machine Systems (IEEE THMS), 2025

2022

  1. Figure from Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian Channels
    IEEE TAC
    Optimal Transmission Power and Controller Design for Networked Control Systems Under State-Dependent Markovian Channels
    Bin Hu and Tua A Tamba
    IEEE Transactions on Automatic Control (IEEE TAC), 2022

2021

  1. Figure from Stochastic stability analysis for Vehicular Networked Systems with State-dependent bursty fading channels: A self-triggered approach
    AUTOM
    Stochastic stability analysis for Vehicular Networked Systems with State-dependent bursty fading channels: A self-triggered approach
    Bin Hu
    Automatica (AUTOM), 2021
  2. Figure from Automation error type and methods of communicating automation reliability affect trust and performance: An empirical study in the cyber domain
    IEEE THMS
    Automation error type and methods of communicating automation reliability affect trust and performance: An empirical study in the cyber domain
    Jing Chen, Scott Mishler, and Bin Hu
    IEEE Transactions on Human-Machine Systems (IEEE THMS), 2021

2019

  1. IEEE TVT
    String stability analysis for vehicle platooning under unreliable communication links with event-triggered strategy
    Zhicheng Li, Bin Hu, Ming Li, and Gengnan Luo
    IEEE Transactions on Vehicular Technology (IEEE TVT), 2019

2018

  1. IJHCS
    The description-experience gap in the effect of warning reliability on user trust and performance in a phishing-detection context
    Jing Chen, Scott Mishler, Bin Hu, Ninghui Li, and Robert W Proctor
    International Journal of Human-Computer Studies (IJHCS), 2018

2017

  1. IEEE RAL
    Optimal task allocation for human–machine collaborative manufacturing systems
    Bin Hu and Jing Chen
    IEEE Robotics and Automation Letters (IEEE RAL), 2017

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

Funded projects

  • NASA Adaptive human-autonomy responsibility allocation for scalable and safe m:N operational architectures 2023–2027 Site PI
  • NSF CHS: Small AI-Human Collaboration in Autonomous Vehicles for Safety and Security 2020–2025 Co-PI