@article{zheng2023differentiable,
  title={Differentiable Trajectory Generation for Car-like Robots with Interpolating Radial Basis Function Networks},
  author={Zheng, Hongrui and Mangharam, Rahul},
  journal={arXiv preprint arXiv:2303.00981},
  year={2023}
}

@article{zheng2022game,
  title={Game-theoretic Objective Space Planning},
  author={Zheng, Hongrui and Zhuang, Zhijun and Betz, Johannes and Mangharam, Rahul},
  journal={arXiv preprint arXiv:2209.07758},
  year={2022}
}

@article{zang2022local_inn,
  title={Local\_INN: Implicit Map Representation and Localization with Invertible Neural Networks},
  author={Zang, Zirui and Zheng, Hongrui and Betz, Johannes and Mangharam, Rahul},
  journal={arXiv preprint arXiv:2209.11925},
  year={2022}
}

@article{evans2022accelerating,
  title={Accelerating Online Reinforcement Learning via Supervisory Safety Systems},
  author={Evans, Benjamin and Betz, Johannes and Zheng, Hongrui and Engelbrecht, Herman A and Mangharam, Rahul and Jordaan, Hendrik W},
  journal={arXiv preprint arXiv:2209.11082},
  year={2022}
}

@article{betz2022teaching,
  title={Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom},
  author={Betz, Johannes and Zheng, Hongrui and Zang, Zirui and Sauerbeck, Florian and Walas, Krzysztof and Dimitrov, Velin and Behl, Madhur and Zheng, Rosa and Biswas, Joydeep and Krovi, Venkat and others},
  journal={arXiv preprint arXiv:2209.11181},
  year={2022}
}

@article{betz2022autonomous,
  title={Autonomous vehicles on the edge: A survey on autonomous vehicle racing},
  author={Betz, Johannes and Zheng, Hongrui and Liniger, Alexander and Rosolia, Ugo and Karle, Phillip and Behl, Madhur and Krovi, Venkat and Mangharam, Rahul},
  journal={IEEE Open Journal of Intelligent Transportation Systems},
  year={2022},
  publisher={IEEE}
}

@inproceedings{bak2022stress,
  title={Stress testing autonomous racing overtake maneuvers with rrt},
  author={Bak, Stanley and Betz, Johannes and Chawla, Abhinav and Zheng, Hongrui and Mangharam, Rahul},
  booktitle={2022 IEEE Intelligent Vehicles Symposium (IV)},
  pages={806--812},
  year={2022},
  organization={IEEE}
}

@inproceedings{zheng2022combinatorial,
  title={Combinatorial and Parametric Gradient-Free Optimization for Cyber-Physical System Design},
  author={Zheng, Hongrui and Betz, Johannes and Ramamurthy, Arun and Jin, Hyunjee and Mangharam, Rahul},
  booktitle={2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION)},
  pages={34--41},
  year={2022},
  organization={IEEE}
}

@article{zheng2022gradient,
  title={Gradient-free Multi-domain Optimization for Autonomous Systems},
  author={Zheng, Hongrui and Betz, Johannes and Mangharam, Rahul},
  journal={arXiv preprint arXiv:2202.13525},
  year={2022}
}

@article{sinha2020formulazero,
  title={FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis},
  author={Sinha, Aman and O'Kelly, Matthew and Zheng, Hongrui and Mangharam, Rahul and Duchi, John and Tedrake, Russ},
  journal={arXiv preprint arXiv:2003.03900},
  year={2020}
}

@InProceedings{pmlr-v123-o-kelly20a,
  title = {textsc{F1TENTH}: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning},
  author = {O'Kelly, Matthew and Zheng, Hongrui and Karthik, Dhruv and Mangharam, Rahul},
  pages = {77--89},
  year = {2020},
  editor = {Hugo Jair Escalante and Raia Hadsell},
  volume = {123},
  series = {Proceedings of Machine Learning Research},
  address = {Vancouver, CA},
  month = {08--14 Dec},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v123/o-kelly20a/o-kelly20a.pdf},
  url = {http://proceedings.mlr.press/v123/o-kelly20a.html},
  abstract = {The deployment and evaluation of learning algorithms on autonomous vehicles (AV) is expensive, slow, and potentially unsafe. This paper details the F1TENTH autonomous racing platform, an open-source evaluation framework for training, testing, and evaluating autonomous systems. With 1/10th-scale low-cost hardware and multiple virtual environments, F1TENTH enables safe and rapid experimentation of AV algorithms even in laboratory research settings. We present three benchmark tasks and baselines in the setting of autonomous racing, demonstrating the flexibility and features of our evaluation environment.} }

@article{otunercar,
  title={TUNERCAR: A Superoptimization Toolchain for Autonomous Racing},
  author={O’Kelly, Matthew and Zheng, Hongrui and Auckley, Joseph and Jain, Achin and Luong, Kim and Mangharam, Rahul}
}

@inproceedings{yao2017monocular,
  title={Monocular vision-based human following on miniature robotic blimp},
  author={Yao, Ningshi and Anaya, Emily and Tao, Qiuyang and Cho, Sungjin and Zheng, Hongrui and Zhang, Fumin},
  booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={3244--3249},
  year={2017},
  organization={IEEE}
}