Haonan Chen

I am a postdoc at Harvard, where I am fortunate to be advised by Yilun Du and work closely with Jiajun Wu. Previously, I was a Ph.D. candidate in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, where I was fortunate to be advised by Katie Driggs-Campbell and was fortunate to work closely with Yunzhu Li.

My research focuses on composable multimodal robot learning for robotic manipulation. I develop structured models that help robots reason about the physical world and learn from heterogeneous data sources, including simulation, teleoperation, human video, and off-policy data.

  • Multi-Modal Perception: Harnessing vision, touch, audio, and language for fine-grained and effective manipulation.
  • Structured World Models: Learning physics-inspired predictive models from and for robotic manipulation of deformable objects.
  • Composable Model Learning: Developing compositional approaches that integrate multiple models for flexible and scalable robot learning.

I'm always excited to explore new collaborations in robotics and machine learning! If you're interested, please reach out via this collaboration form. I'd love to chat!

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Publications

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Multi-Modal Manipulation via Multi-Modal Policy Consensus
Haonan Chen, Jiaming Xu*, Hongyu Chen*, Kaiwen Hong, Binghao Huang, Chaoqi Liu, Jiayuan Mao, Yunzhu Li, Yilun Du+, and Katherine Driggs-Campbell+
* Equal contribution, + Equal advising
[Project] [Paper] [Code] [Dataset] [Video] [Audio] [Blog] [Deepwiki]
Featured in Video Friday on [IEEE Spectrum]
Tool-as-Interface: Learning Robot Policies from Observing Human Tool Use
Haonan Chen, Cheng Zhu, Shuijing Liu, Yunzhu Li, and Katherine Driggs-Campbell
CoRL 2025, [Project] [Paper] [Code]
Best Paper Award at ICRA 2025 Workshop on Foundation Models and Neuro-Symbolic AI for Robotics [Link]
Best Presentation Award at CSL Student Conference 2025 [Link]
Featured in Video Friday on [IEEE Spectrum]
Media Coverage: [The Grainger College of Engineering], [TechXplore], [Hackster.io]
Towards Uncertainty Unification: A Case Study for Preference Learning
Shaoting Peng, Haonan Chen, and Katherine Driggs-Campbell
RSS 2025, [Paper]
Learning Coordinated Bimanual Manipulation Policies using State Diffusion and Inverse Dynamics Models
Haonan Chen, Jiaming Xu*, Lily Sheng*, Tianchen Ji, Shuijing Liu, Yunzhu Li, and Katherine Driggs-Campbell
ICRA 2025, [Project] [Paper]
Cooperative Advisory Residual Policies for Congestion Mitigation
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, and Katherine Driggs-Campbell
JATS 2024, [Paper]
Lessons in Cooperation: A Qualitative Analysis of Driver Sentiments towards real-time Advisory Systems through a Focus Group User Study
Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, and Katherine Driggs-Campbell
ITSM 2024, [Paper]
Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks
Haonan Chen, Yilong Niu*, Kaiwen Hong*, Shuijing Liu, Yiqing Wang, Yunzhu Li, and Katherine Driggs-Campbell
CoRL 2023, [Project] [Paper]
Finalist - Best Paper/Best Student Paper Awards
Towards Safety of Multi-Level Human-Robot Interaction in Industrial Tasks
Zhe Huang, Yeji Mun, Haonan Chen, Yiqing Xie, Yilong Niu, Xiang Li, Ninghan Zhong, Haoyuan You, Daniel L. McPherson, and Katherine Driggs-Campbell
CASE 2023 (Special Session)
Learning Task Skills and Goals Simultaneously from Physical Interaction
Haonan Chen*, Yeji Mun*, Zhe Huang, Yilong Niu, Yiqing Xie, D. Livingston McPherson, and Katherine Driggs-Campbell
CASE 2023 (Special Session)
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
Aamir Hasan, Neeloy Chakraborty*, Haonan Chen*, Jung-Hoon Cho, Cathy Wu, and Katherine Driggs-Campbell
ITSC 2023, [Paper] [Website]

Best Presentation Award at CSL Student Conference 2024

Combining Model-Based Controllers and Generative Adversarial Imitation Learning for Traffic Simulation
Haonan Chen, Tianchen Ji, Shuijing Liu, and Katherine Driggs-Campbell
ITSC 2022, [Paper]
Learning to Navigate Intersections with Unsupervised Driver Trait Inference
Shuijing Liu, Peixin Chang, Haonan Chen, Neeloy Chakraborty, and Katherine Driggs-Campbell
ICRA 2022, [Paper] [Website] [Video]
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control
Peixin Chang, Shuijing Liu, Haonan Chen, and Katherine Driggs-Campbell
IROS 2020, [Paper] [Video]

Awards and Honors

  • Conference Travel Award, Graduate College, University of Illinois Urbana-Champaign 2023
  • Finalist - Best Paper/Best Student Paper Awards, Conference on Robot Learning 2023
  • Bronze Tablet Award, University of Illinois Urbana-Champaign 2020
  • Excellent Graduate, Zhejiang University 2020
  • Student Speaker at Graduation, ZJU-UIUC Institute 2020
  • Provincial Scholarship, Zhejiang Province, China (Top 3%) 2019
  • Dean's List, University of Illinois Urbana-Champaign 2019
  • National Scholarship, China (Top 1.8%) 2018
  • Excellent Undergraduate Scholarship, Zhejiang University (Top 3%) 2018-2019
  • Academic Excellence Scholarship Award, ZJU-UIUC Institute 2017-2019
  • Top Ten Social Practice, Zhejiang University (Top 1.5%) 2017

Service and Leadership

Professional Service

  • Session Chair:
    • CASE - IEEE Conference on Automation Science and Engineering, 2023
  • Reviewer:
    • CoRL - Conference on Robot Learning
    • ICRA - IEEE International Conference on Robotics and Automation
    • ICLR - International Conference on Learning Representations
    • RA-L - IEEE Robotics and Automation Letters
    • ITSC - IEEE International Conference on Intelligent Transportation Systems
    • CASE - IEEE Conference on Automation Science and Engineering

Extra-Curricular Service

Founding Team Leader, Robotic Team for RoboMaster Challenge, ZJU-UIUC Institute 10/2017 - 06/2018


Thanks Jon Barron for this amazing template.