About Me

I am a second-year Ph.D. student at University of California, Berkeley, working with Prof. Masayoshi Tomizuka. I am also a part-time AI resident at X, the moonshot factory (formerly Google[x]). My current research interests lie at designing reward learning algorithms to make the robot better interact with the environment.

Before coming to UC Berkeley, I received my B.S. degree from Shanghai Jiao Tong University in 2019. During my undergraduate years, I was fortunate to work with Cewu Lu at SJTU, Jiajun Wu and Josh Tenenbaum at MIT.


  1. Learning dense reward for contact-rich manipulation tasks.
    Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar, Masayoshi Tomizuka, Stefan Schaal
    Submitted to the International Conference on Robotics and Automation (ICRA), 2021

  2. Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving.
    Zheng Wu*, Liting Sun*, Wei Zhan, Chenyu Yang, Masayoshi Tomizuka (* denotes equal contribution)
    IEEE Robotics and Automation Letters (RA-L)

  3. Expressing diverse human driving behavior with probabilistic rewards and online inference.
    Liting Sun*, Zheng Wu*, Hengbo Ma, Masayoshi Tomizuka (* denotes equal contribution)
    Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020

  4. See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion.
    Nima Fazeli , Miquel Oller, Jiajun Wu, Zheng Wu, Joshua B Tenenbaum, Alberto Rodriguez
    Science Robotics
    Paper / Video / MIT News / BBC / CNN

  5. Learning to describe scenes with programs.
    Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B Tenenbaum, Jiajun Wu
    International Conference on Learning Representations (ICLR), 2019

  6. Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters.
    Zheng Wu, Ruiheng Chang, Jiaxu Ma, Cewu Lu, Chi-Keung Tang
    International Joint Conference on Artificial Intelligence (IJCAI), 2018
    Paper / Slides