Johns Hopkins University · Department of Computer Science · Data Science and AI Institute · Laboratory for Computational Sensing and Robotics · Institute for Assured Autonomy

Publications

2026

  • AI Coaching for Accelerating Human Skill Development with Reinforcement Learning

    Wei Wang, Enlin Gu, Antonio Loquercio, Haimin Hu†, and Rahul Mangharam†

    Conference on Robot Learning (CoRL), 2026

    Paper Homepage

  • Provably Optimal Reinforcement Learning under Safety Filtering

    Donggeon David Oh, Duy Phuong Nguyen, Haimin Hu, and Jaime Fernández Fisac

    Proceedings of the IASEAI Conference, 2026

    Paper Code

  • RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation

    Kaiqu Liang, Haimin Hu, Ryan Liu, Thomas L. Griffiths, and Jaime Fernández Fisac

    Findings of the Association for Computational Linguistics (ACL), 2026

    Paper Code

  • Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

    Haimin Hu

    Algorithmic Foundations of Robotics XVII (WAFR), 2026

    Paper Code

  • Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning

    Donggeon David Oh, Duy Phuong Nguyen, Haimin Hu, and Jaime Fernández Fisac

    IEEE Conference on Decision and Control (CDC), 2026

    Paper

  • Agents’ Last Exam

    Yiyou Sun, Xinyang Han, Weichen Zhang, Yuanbo Pang, Tianyu Wang, Yuhan Cao, Yixiao Huang, Chris Duroiu, Haoyun Zhang, Jeffrey Lin, et al.

    arXiv:2606.05405, 2026

    Paper

2025

  • Game-Theoretic Integration of Safety and Learning for Human-Centered Robotics

    Haimin Hu

    Princeton University (Dissertation), 2025

    Paper

  • Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models

    Kaiqu Liang, Haimin Hu, Xuandong Zhao, Dawn Song, Thomas L. Griffiths, and Jaime Fernández Fisac

    arXiv:2507.07484, 2025

    Paper

  • Gambits or Assurances? Towards Robust and Verifiable Intelligence for Human-Centered Robotics

    Haimin Hu

    Robotics: Science and Systems (RSS) Pioneers Workshop, 2025

    Paper

  • Safety with Agency: Human-Centered Safety Filter with Application to AI-Assisted Motorsports

    Donggeon David Oh*, Justin Lidard*, Haimin Hu, Himani Sinhmar, Elle Lazarski, Deepak Gopinath, Emily Sumner, Jonathan DeCastro, Guy Rosman, Naomi Leonard, and Jaime Fernández Fisac

    Robotics: Science and Systems (RSS), 2025

    Paper Homepage

  • Think Deep and Fast: Learning Neural Nonlinear Opinion Dynamics from Inverse Dynamic Games for Split-Second Interactions

    Haimin Hu, Jaime Fernández Fisac, Naomi E. Leonard, Deepak Gopinath, Jonathan DeCastro, and Guy Rosman

    IEEE International Conference on Robotics and Automation (ICRA), 2025

    Paper Poster

2024 and Before

  • MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety

    Justin Wang*, Haimin Hu*, Duy Phuong Nguyen, and Jaime Fernández Fisac

    Algorithmic Foundations of Robotics XVI (WAFR), 2024

    Paper Code Video

  • Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach

    Haimin Hu, David Isele, Sangjae Bae, and Jaime F. Fisac

    The International Journal of Robotics Research (IJRR), vol. 43, no. 9, 2024

    Paper Video

  • Doxo-Physical Planning: A New Paradigm for Safe and Efficient Human-Robot Interaction under Uncertainty

    Haimin Hu

    Companion of the ACM/IEEE International Conference on Human-Robot Interaction (HRI Pioneers Workshop), 2024

    Paper

  • Blending Data-Driven Priors in Dynamic Games

    Justin Lidard*, Haimin Hu*, Asher Hancock, Zixu Zhang, Albert Gimó Contreras, Vikash Modi, Jonathan DeCastro, Deepak Gopinath, Guy Rosman, Naomi Leonard, María Santos, and Jaime Fernández Fisac

    Robotics: Science and Systems (RSS), 2024

    Paper Code Homepage

  • Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots

    Haimin Hu*, Gabriele Dragotto*, Zixu Zhang, Kaiqu Liang, Bartolomeo Stellato, and Jaime Fernández Fisac

    Robotics: Science and Systems (RSS), 2024

    Paper Code Homepage

  • The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems

    Kai-Chieh Hsu, Haimin Hu, and Jaime F. Fisac

    Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), vol. 7, 2024

    Paper

  • Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics

    Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, Naomi E. Leonard, and Jaime F. Fisac

    IEEE Conference on Decision and Control (CDC), 2023

    Nominated for the Roberto Tempo Best CDC Paper Award

    Paper Code Homepage

  • Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy

    Haimin Hu*, Zixu Zhang*, Kensuke Nakamura, Andrea Bajcsy, and Jaime Fernández Fisac

    Conference on Robot Learning (CoRL), 2023

    Best Presentation Award at the 2024 RSS Safe Autonomy Workshop

    Paper Code Homepage

  • Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

    Haimin Hu and Jaime F. Fisac

    Algorithmic Foundations of Robotics XV (WAFR), 2022

    Invited Extension for IJRR Special Issue

    Paper Code Video Homepage

  • SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction

    Haimin Hu, Kensuke Nakamura, and Jaime F. Fisac

    IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 2, 2022

    Paper Code Video Homepage Poster

  • FaSTrack: A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

    Mo Chen*, Sylvia L. Herbert*, Haimin Hu, Ye Pu, Jaime F. Fisac, Somil Bansal, SooJean Han, and Claire J. Tomlin

    IEEE Transactions on Automatic Control (TAC), vol. 66, no. 12, 2021

    Paper Slides

  • Learning Hybrid Control Barrier Functions from Data

    Lars Lindemann, Haimin Hu, Alexander Robey, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, and Nikolai Matni

    Conference on Robot Learning (CoRL), 2020

    Paper Code Video

  • Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming

    Haimin Hu, Mahyar Fazlyab, Manfred Morari, and George J. Pappas

    IEEE Conference on Decision and Control (CDC), 2020

    Paper

  • Learning Control Barrier Functions from Expert Demonstrations

    Alexander Robey*, Haimin Hu*, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, and Nikolai Matni

    IEEE Conference on Decision and Control (CDC), 2020

    Paper Code

  • Non-Cooperative Distributed MPC with Iterative Learning

    Haimin Hu, Konstantinos Gatsis, Manfred Morari, and George J. Pappas

    21st IFAC World Congress, 2020

    Video Slides

  • Tuning Communication Latency for Distributed Model Predictive Control

    Haimin Hu, Konstantinos Gatsis, Manfred Morari, and George J. Pappas

    IFAC Conference on Distributed Estimation and Control in Networked Systems (NecSys), 2019

    Paper Poster

  • Min-max Differential Inequalities for Polytopic Tube MPC

    Xuhui Feng, Haimin Hu, Mario E. Villanueva, and Boris Houska

    American Control Conference (ACC), 2019

    Paper Video Slides

  • Plug and Play Distributed Model Predictive Control for Heavy Duty Vehicle Platooning and Interaction with Passenger Vehicles

    Haimin Hu, Ye Pu, Mo Chen, and Claire J. Tomlin

    IEEE Conference on Decision and Control (CDC), 2018

    Paper Slides

  • Real-Time Tube MPC Applied to a 10-State Quadrotor Model

    Haimin Hu, Xuhui Feng, Rien Quirynen, Mario E. Villanueva, and Boris Houska

    American Control Conference (ACC), 2018

    Paper Slides