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Tonghe Zhang

I am a Master of Science student in Robotics at Robotics Institute, Carnegie Mellon University, advised by Professors Guanya Shi and Max Simchowitz.

I study how to build scalable robot systems that act efficiently and reliably in the real world.

Email: tonghez[AT]andrew.cmu.edu

• 
  Think on Your Feet: Seamless Transition between Human-like Locomotions in Response to Changing Commands

  Huaxing Huang*, Wenhao Cui*, Tonghe Zhang*, Shengtao Li, Chenxu Hu, Tianchu Zhang, Liang Zheng, Ziyang Tang, Jinchao Han, Bangyu Qin, Tianchu Zhang, Shipu Zhang, Zheyuan Jiang.

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

  TL;DR: Using GAN-based imitation and sim2real RL to teach a humanoid robot to interpolate between various human-like locomotion skills.

  IEEE Xplore arXiv Google Scholar BibTex

  @INPROCEEDINGS{11127948,
    author={Huang, Huaxing and Cui, Wenhao and Zhang, Tonghe and Li, Shengtao and Han, Jinchao and Qin, Bangyu and Zhang, Tianchu and Zheng, Liang and Tang, Ziyang and Hu, Chenxu and Zhang, Shipu and Jiang, Zheyuan},
    booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, 
    title={Think on Your Feet: Seamless Transition Between Human-Like Locomotion in Response to Changing Commands}, 
    year={2025},
    volume={},
    number={},
    pages={15965-15971},
    keywords={Adaptation models;Tracking;Imitation learning;Stability criteria;Humanoid robots;Multitasking},
    doi={10.1109/ICRA55743.2025.11127948}}

• 
  Provably Efficient Partially-observable Risk-sensitive Reinforcement Learning

  Tonghe Zhang*, Yu Chen*, Longbo Huang.

  Proceedings of the 41st International Conference on Machine Learning (ICML 2024).

  TL;DR: Establishing a statistical theory for the sample complexity of risk-sensitive RL under partial observability.

  PMLR arXiv Google Scholar GitHub BibTex

  @InProceedings{pmlr-v235-zhang24g,
    title = 	 {Provably Efficient Partially Observable Risk-sensitive Reinforcement Learning with Hindsight Observation},
    author =       {Zhang, Tonghe and Chen, Yu and Huang, Longbo},
    booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
    pages = 	 {58680--58716},
    year = 	 {2024},
    editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
    volume = 	 {235},
    series = 	 {Proceedings of Machine Learning Research},
    month = 	 {21--27 Jul},
    publisher =    {PMLR},
    pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24g/zhang24g.pdf},
    url = 	 {https://proceedings.mlr.press/v235/zhang24g.html},
  }

Preprints

• 
  π_RL: Online RL Fine-tuning for Flow-based Vision-Language-Action Models

  Kang Chen*, Zhihao Liu*, Tonghe Zhang*, Zhen Guo, Si Xu, Hao Lin, Hongzhi Zang, Quanlu Zhang, Zhaofei Yu, Guoliang Fan, Tiejun Huang, Yu Wang, Chao Yu.

  Preprint.

  TL;DR: Online RL fine-tuning for flow-based VLAs (π0, π0.5) with policy gradient, achieving high success rate in LIBERO, ManiSkill, MetaWorld via large-scale parallel simulation.

  arXiv GitHub HuggingFace Google Scholar BibTex

  @misc{chen2025pitextttrlonlinerlfinetuning,
        title={$\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models}, 
        author={Kang Chen and Zhihao Liu and Tonghe Zhang and Zhen Guo and Si Xu and Hao Lin and Hongzhi Zang and Quanlu Zhang and Zhaofei Yu and Guoliang Fan and Tiejun Huang and Yu Wang and Chao Yu},
        year={2025},
        eprint={2510.25889},
        archivePrefix={arXiv},
        primaryClass={cs.LG},
        url={https://arxiv.org/abs/2510.25889}, 
  }