Short Bio

I am currently a fifth-year Ph.D. student advised by Prof. Hao Su at the Computer Science & Engineering (CSE), University of California, San Diego. My general research interests cover reinforcement learning, generative models, robotics and soft body manipulation.

I have got my bachelor's degree at Tsinghua University, where I worked at the Machine Intelligence Group, advised by Prof. Chongjie Zhang. I spent most of my time being an intern at Megvii Inc. (Face++). During the spring of 2017, I was a visiting student at the University of Michigan, advised by Prof. Jia Deng. I was a summer intern at MIT-IBM Watson AI Lab working with Chuang Gan from 2020 to 2022.

Education

• Ph.D. in Computer Science, University of California, San Diego, 2018 - present

• B.E. in Computer Science, Tsinghua University, 2014 - 2018

Publication

• Reparameterized Policy Learning for Multimodal Trajectory Optimization

       Zhiao Huang, Litian Liang, Zhan Ling, Xuanlin Li, Chuang Gan, Hao Su

       International Conference on Machine Learning 2023 oral

       [project page]

• Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization

      Stone Tao, Xiaochen Li, Tongzhou Mu, Zhiao Huang, Yuzhe Qin, Hao Su

      International Conference on Machine Learning 2023

      [project page]

• RoboNinja: Learning an Adaptive Cutting Policy for Multi-Material Objects

Zhenjia Xu, Zhou Xian, Xingyu Lin, Cheng Chi, Zhiao Huang, Chuang Gan, Shuran Song

Robotics: Science and Systems 2022

[project page]

• ManiSkill2: A Unifified Benchmark for Generalizable Manipulation Skills

Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, Hao Su

International Conference on Learning Representations (ICLR), 2023 (Spotlight)

[project page]

• Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation

Xingyu Lin, Carl Qi, Yunchu Zhang, Yunzhu Li, Zhiao Huang, Katerina Fragkiadaki, Chuang Gan, David Held

Conference on Robot Learning (CoRL) 2022

[project page]

• Learning Multi-Object Dynamics with Compositional Neural Radiance Fields

Danny Driess, Zhiao Huang, Yunzhu Li, Russ Tedrake, Marc Toussaint

Conference on Robot Learning (CoRL) 2022

[arxiv]

• RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

      Haochen Shi*, Huazhe Xu*,  Zhiao Huang, Yunzhu Li, Jiajun Wu

      Robotics: Science and Systems (RSS), 2022.

      [code] [project page]

• DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

      Xingyu Lin, Zhiao Huang, Yunzhu Li, Joshua B Tenenbaum, David Held, Chuang Gan

      International Conference on Learning Representations (ICLR), 2022.

      [paper]

• Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

      Sizhe Li*, Zhiao Huang*, Tao Du, Hao Su, Joshua B Tenenbaum, Chuang Gan

      International Conference on Learning Representations (ICLR), 2022 (Spotlight).

      [paper]

• Efficient Hierarchical Navigation and Manipulation by Constraint-induced Option-Reward Design

Zhiao Huang, Xiaochen Li and Hao Su

RSS 2021 Workshop on Declarative and Neurosymbolic Representations in Robot Learning and Control

[paper]

• ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations

Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, Hao Su

[paper]

• PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan

International Conference on Learning Representations (ICLR), 2021 (Spotlight) .

[paper] [project page]

• Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks

Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su

Neural Information Processing Systems (NeurIPS), 2020.

[paper]

• Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories

Tiange Luo, Kaichun Mo, Zhiao Huang, Jiarui Xu, Siyu Hu, Liwei Wang, Hao Su

International Conference on Learning Representations (ICLR), 2020.

[paper]

• Mapping State Space using Landmarks for Universal Goal Reaching

Zhiao Huang*, Fangchen* Liu, Hao Su

Neural Information Processing Systems (NeurIPS), 2019.

[paper]

• Object-oriented dynamics predictor

Guangxiang Zhu, Zhiao Huang, Chongjie Zhang 

Neural Information Processing Systems (NeurIPS), 2018.

[paper]

• Associative Embedding: End-to-End Learning for Joint Detection and Grouping

Alejandro Newell, Zhiao Huang, Jia Deng

Neural Information Processing Systems (NIPS), 2017.

[paper]