I am a fourth-year Ph.D. student in Computer Science at the University of Texas at Austin. I am fortunate to be advised by Professor Yuke Zhu, and I am a member of the Robot Perception and Learning Lab. My research interests span the fields of Robotics and Machine Learning. My research goal is to develop algorithms that facilitate efficient robot learning via human-robot partnership, and to build scalable, trustworthy robotics systems for long-term deployment.

I previously did my undergraduate in Computer Science at the University of California, Berkeley, and I was affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. I had the pleasure to work with Professor Sergey Levine and Professor Costas Spanos.

News & Highlights

• [July 2024] Our paper Sirius (Extended Version) is accepted at IJRR.
• [June 2024] I am working as a Research Scientist Intern at FAIR Embodied AI.
• [Nov 2023] We are organizing a workshop on Towards Reliable and Deployable Learning-Based Robotic Systems at CoRL 2023.
• [Jul 2023] Our paper Sirius is nominated as a Best Paper Award Finalist at RSS 2023.
• [May 2022] Our paper MAPLE won the Outstanding Learning Paper award at ICRA 2022.

Publications

• Huihan Liu, Yu Zhang, Vaarij Betala, Evan Zhang, James Liu, Crystal Ding, Yuke Zhu
  Multi-Task Interactive Robot Fleet Learning with Visual World Models
  Conference on Robot Learning (CoRL), 2024
  [PDF] [Website] [BibTeX]
  

  @inproceedings{liumulti,
        title={Multi-Task Interactive Robot Fleet Learning with Visual World Models},
        author={Liu, Huihan and Zhang, Yu and Betala, Vaarij and Zhang, Evan and Liu, James and Ding, Crystal and Zhu, Yuke},
        booktitle={8th Annual Conference on Robot Learning}
      }
    

• Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu
  Robot Learning on the Job: Human-in-the-Loop Manipulation and Learning During Deployment (Extended Version)
  The International Journal of Robotics Research (IJRR), 2024
  [PDF] [Website] [BibTeX]
  

      @inproceedings{liu2022robot,
      title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment},
      author = {Huihan Liu and Soroush Nasiriany and Lance Zhang and Zhiyao Bao and Yuke Zhu},
      booktitle = {Robotics: Science and Systems (RSS)},
      year = {2023}
      }
    

• Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu
  PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning
  IEEE Robotics and Automation Letters (RA-L), 2024
  [PDF] [Website] [BibTeX]
  

  @misc{gao2024prime,
        title={PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning}, 
        author={Tian Gao and Soroush Nasiriany and Huihan Liu and Quantao Yang and Yuke Zhu},
        year={2024},
        eprint={2403.00929},
        archivePrefix={arXiv},
        primaryClass={cs.RO}
  }
  

• Huihan Liu, Shivin Dass, Roberto Martín-Martín, Yuke Zhu
  Model-Based Runtime Monitoring with Interactive Imitation Learning
  IEEE International Conference on Robotics and Automation (ICRA), 2024
  [PDF] [Website] [BibTeX]
  

    @misc{liu2023modelbased,
      title={Model-Based Runtime Monitoring with Interactive Imitation Learning}, 
      author={Huihan Liu and Shivin Dass and Roberto Martín-Martín and Yuke Zhu},
      year={2023},
      eprint={2310.17552},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
  }
  

• Huihan Liu, Alice Chen, Yuke Zhu, Adith Swaminathan, Andrey Kolobov, Ching-An Cheng
  Interactive Robot Learning from Verbal Correction
  CoRL Workshop on Language and Robot Learning (LangRob), 2023
  [PDF] [Website] [BibTeX]
  

    @misc{liu2023interactive,
      title={Interactive Robot Learning from Verbal Correction}, 
      author={Huihan Liu and Alice Chen and Yuke Zhu and Adith Swaminathan and Andrey Kolobov and Ching-An Cheng},
      year={2023},
      eprint={2310.17555},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
  }
  

• Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu
  Robot Learning on the Job: Human-in-the-Loop Manipulation and Learning During Deployment
  Robotics: Science and Systems (RSS), 2023
  [PDF] [Website] [BibTeX]
  Best Paper Award Finalist
  

      @inproceedings{liu2022robot,
      title = {Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment},
      author = {Huihan Liu and Soroush Nasiriany and Lance Zhang and Zhiyao Bao and Yuke Zhu},
      booktitle = {Robotics: Science and Systems (RSS)},
      year = {2023}
      }
    

• Soroush Nasiriany, Huihan Liu, Yuke Zhu
  Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
  IEEE International Conference on Robotics and Automation (ICRA), 2022
  [PDF] [Website] [BibTeX]
  Outstanding Learning Paper
  

      @misc{nasiriany2021augmenting,
        title={Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks}, 
        author={Soroush Nasiriany and Huihan Liu and Yuke Zhu},
        year={2021},
        eprint={2110.03655},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }
    

• Avi Singh*, Huihan Liu*, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine
  Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
  International Conference on Learning Representations (ICLR), 2021
  [PDF] [Website] [BibTeX]
  Oral Presentation (top 1.8% of submissions)
  

      @misc{singh2020parrot,
        title={Parrot: Data-Driven Behavioral Priors for Reinforcement Learning}, 
        author={Avi Singh and Huihan Liu and Gaoyue Zhou and Albert Yu and Nicholas Rhinehart and Sergey Levine},
        year={2020},
        eprint={2011.10024},
        archivePrefix={arXiv},
        primaryClass={cs.LG}
  }
    

• Han Zou, Yuxun Zhou, Jianfei Yang, Huihan Liu, Hari Prasanna Das, Costas J. Spanos
  Consensus Adversarial Domain Adaptation
  AAAI Conference on Artificial Intelligence, 2019
  [PDF] [BibTeX]
  

      @inproceedings{Zou2019ConsensusAD,
          title={Consensus Adversarial Domain Adaptation},
          author={Han Zou and Yuxun Zhou and Jianfei Yang and Huihan Liu and H. Das and C. Spanos},
          booktitle={AAAI},
          year={2019}
          }
      }
    

• Han Zou, Jianfei Yang, Hari Prasanna Das, Huihan Liu, Yuxun Zhou, Costas J. Spanos
  WiFi and Vision Multimodal Learning for Accurate and Robust Device-Free Human Activity Recognition
  Multimodal Learning and Applications (MULA) Workshop, Conference on Computer Vision and Pattern Recognition (CVPR), 2019
  [PDF] [BibTeX]
  

      @InProceedings{Zou_2019_CVPR_Workshops,
          author = {Zou, Han and Yang, Jianfei and Prasanna Das, Hari and Liu, Huihan and Zhou, Yuxun and Spanos, Costas J.},
          title = {WiFi and Vision Multimodal Learning for Accurate and Robust Device-Free Human Activity Recognition},
          booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
          month = {June},
          year = {2019}
      }
    

• Ioannis Konstantakopoulos, Andrew R.Barkan, Shiying He, Tanya Veeravalli, Huihan Liu, Costas J. Spanos
  A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
  Applied Energy Journal, Volume 237, 1 March 2019
  [PDF] [BibTeX]
  

      @misc{konstantakopoulos2018deep,
            title={A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University}, 
            author={Ioannis C. Konstantakopoulos and Andrew R. Barkan and Shiying He and Tanya Veeravalli and Huihan Liu and Costas Spanos},
            year={2018},
            eprint={1809.05142},
            archivePrefix={arXiv},
            primaryClass={cs.LG}
      }