About Me

I am working on Robot Policy Learning and Geometric Learning at The Helping Hands Lab in Northeastern University, advised by Professor Robert Platt and PhD Candidate Haojie Huang. Previously, I conducted research on multiple robotic and computer vision topics: Deep Learning Optimization for 3D Vision, FDM Printing for Soft Robotics, and Healthcare Automated Monitoring System. My past research works are supervised by Prof. Ziming Zhang at VISLab@WPI, Prof. Markus P. Nemitz at Nemitz Robotics Group, and Prof. Christopher Nycz at AIM Lab. CV: Link. Contact: seanliu0272 [At] gmail [Dot] com

News

  • June 30, 2024: Our paper, “Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance,” has been accepted for an Oral Presentation at IEEE/RSJ IROS 2024.
  • January 31, 2024: Our paper, “Vision-based FDM Printing for Fabricating Airtight Soft Actuators,” has been accepted for an Oral Presentation at IEEE RoboSoft 2024.

Research Vision

My long-term research goal is to build automation systems that can adapt reliably to new environments through trustworthy perception and learned motion strategies grounded in structured reasoning. These systems will assist humans in accomplishing challenging tasks in the physical world.

Selected Publications

  • Policy Learning for Robotic Manipulation

    • Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation

    Haojie Huang, Linfeng Zhao, Haotian Liu, Zhang Ye, Si-Yuan Huang, Mingxi Jia, Boce Hu, Fangzhou Lin, Yu Qi, Dian Wang, Robin Walters*, Robert Platt* (* Equal Advising)

    In Submission, Paper, Project Page

    Pix2Act

    MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

    Haojie Huang, Haotian Liu, Dian Wang, Robin Walters*, and Robert Platt* (* Equal Advising)

    IEEE International Conference on Robotics and Automation ICRA 2025 at Atlanta USA, Paper, Project Page

    Match Policy

    IMAGINATION POLICY: Using Generative Point Cloud Models for Learning Manipulation Policies

    Haojie Huang, Karl Schmeckpeper*, Dian Wang*, Ondrej Biza*, Yaoyao Qian**, Haotian Liu**, Mingxi Jia**, Robert Platt, and Robin Walters (*, ** Equal Contribution)

    Conference on Robot Learning CoRL 2024 at Munich, Germany, Paper, Project Page

    Imagination Policy

  • Deep Learning Optimization for 3D Vision

    • GPS: A Probabilistic Distributional Similarity with Gumbel Priors for Set-to-Set Matching

    Haotian Liu*, Fangzhou Lin*, Ziming Zhang*, Jose Morales, Haichong Zhang, Kazunori Yamada, Vijaya B Kolachalama, Venkatesh Saligrama (* co-first author)

    International Conference on Learning Representations ICLR 2025 at Singapore, Paper, Code

    GPS

    Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

    Haotian Liu*, Fangzhou Lin*, Haoying Zhou*, Songlin Hou*, Kazunori Yamada, Gregory S. Fischer, Yanhua Li, Haichong K. Zhang, and Ziming Zhang (* co-first author)

    IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2024 at Abu Dhabi UAE, Oral Presentation, Paper, Code, Presentation

    Loss Distillation

    Services

    Reviewer of: NeurIPS, ICLR, AISTATS, ICML