Tongzhou Mu 木同舟

Tongzhou Mu 


Ph.D. candidate,
Department of Computer Science and Engineering,
University of California San Diego

Email: t3mu [@] ucsd [DOT] edu

X(Twitter) / Google Scholar / DBLP / Github / LinkedIn


experience 


I am looking for both full-time jobs and internships. Please feel free to contact me!

About me

I am a Ph.D. candidate at the University of California San Diego, and I am fortunate to be advised by Prof. Hao Su. My research focuses on modeling the world for AI agents (see this figure for a brief overview). Specifically, I am interested in Reinforcement Learning / Imitation Learning, Robotics / Embodied AI, and Reasoning / Planning.

Prior to starting as a Ph.D. student, I received my B.Eng. and M.Sc. degrees in computer science from Zhejiang University, and UC San Diego in 2017 and 2019, respectively. I have also spent time at NVIDIA Robotics Lab, Amazon Alexa AI, Wormpex AI, Intel AI, and Microsoft Research Asia.

I am the major developer of the 1st version of ManiSkill benchmark, and the lead organizer of the ManiSkill Challenge 2021 and the Generalizable Policy Learning in the Physical World Workshop. I also actively contribute to the development of the subsequent generations of ManiSkill, e.g., ManiSkill 2 and ManiSkill 3.

Research interests

My research interests include

  • Reinforcement Learning / Imitation Learning

    • Learning from Demonstrations (offline and online)

    • Sturctured Policies and Neural Programs

  • Robotics / Embodied AI

    • Foundation Models for Embodied AI

    • Motion Synthesis with Generative Models

    • Creating Tasks, Assets, and Demontrations in Simulation

  • Reasoning / Planning

    • Solving Challenging Reasoning Problems with LLMs

    • Long-horizon Planning with Hierarchical Policies

Publications & Preprints

Papers sorted by years. Representative papers are highlighted.

2024

adademo 

AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
Tongzhou Mu, Yijie Guo, Jie Xu, Ankit Goyal, Hao Su, Dieter Fox, Animesh Garg
Submitted to IROS 2024
[arXiv]

s2v_dagger 

When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Tongzhou Mu*, Zhaoyang Li*, Stanisław Wiktor Strzelecki*, Xiu Yuan, Yunchao Yao, Litian Liang, Hao Su
Submitted to RLC 2024
[Manuscript]

llm_explore 

Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Zhan Ling, Yunhao Fang, Xuanlin Li, Tongzhou Mu, Mingu Lee, Reza Pourreza, Roland Memisevic, Hao Su
Submitted to ICML 2024
[arXiv] [Code]

drs 

DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks
Tongzhou Mu, Minghua Liu, Hao Su
International Conference on Learning Representations (ICLR) 2024
[Project Page]

2023

no_pretrain 

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Nicklas Hansen*, Zhecheng Yuan*, Yanjie Ze*, Tongzhou Mu* , Aravind Rajeswaran+, Hao Su+, Huazhe Xu+, Xiaolong Wang+
International Conference on Machine Learning (ICML) 2023
[arXiv] [Code]

skill_trans 

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 (ICML) 2023
[Project Page] [arXiv] [Video] [Code] [Slides]
* Stone Tao and Xiaochen Li are undergruduate students I advised

demo_rl 

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey
Tongzhou Mu, Hao Su
ArXiv preprint
[arXiv] [Slides]

maniskill2 

ManiSkill2: A Unified 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
* equally contributed authors are ordered by alphabets
International Conference on Learning Representations (ICLR) 2023
[Project Page] [arXiv] [Code] [Challenge Website]

active 

Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation
Xiaoshuai Zhang, Rui Chen, Fanbo Xiang, Yuzhe Qin, Jiayuan Gu, Zhan Ling, Minghua Liu, Peiyu Zeng, Songfang Han, Zhiao Huang, Tongzhou Mu, Jing Xu, Hao Su
IEEE Transactions on Robotics (T-RO) 2023
[arXiv]

2022

inter_rl 

Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
Tongzhou Mu, Kaixiang Lin, Feiyang Niu, Govind Thattai
Transactions on Machine Learning Research (TMLR) 2022
[arXiv]

2021

ManiSkill 

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
Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track 2021
[arXiv] [Video] [Slides] [Poster] [Code] [Baselines] [Challenge Website]

arena 

Arena: A Scalable and Configurable Benchmark for Policy Learning
Sirui Xu, Shuang Liu, Tongzhou Mu, Zhiwei Jia, Yiran Wu, Hao Su
Preprint
[Code]

2020

policy_refactorization 

Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals
Tongzhou Mu* , Jiayuan Gu*, Zhiwei Jia, Hao Tang, Hao Su
Conference on Neural Information Processing Systems (NeurIPS) 2020
[Project Page] [arXiv] [Poster] [Slides] [Code]

sail 

State Alignment-based Imitation Learning
Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su
International Conference on Learning Representations (ICLR) 2020
[arXiv] [Code]


Before 2019

value_transfer 

Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning
Xingchao Liu*, Tongzhou Mu* , Hao Su
Deep Reinforcement Learning Workshop at NeurIPS 2018

doubly_sgd 

Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Zebang Shen, Hui Qian, Tongzhou Mu, Chao Zhang
International Joint Conference on Artificial Intelligence (IJCAI) 2017

adaptive_vr 

Adaptive Variance Reducing for Stochastic Gradient Descent
Zebang Shen, Hui Qian, Tengfei Zhou, Tongzhou Mu
International Joint Conference on Artificial Intelligence (IJCAI) 2016

Talks

Professional Services

Teaching

  • Co-Instructor: CSE 276F Machine Learning for Robotics at UC San Diego, Spring 2024

  • Guest Lecturer: CSE 291-A00 Machine Learning for Robotics at UC San Diego, Winter 2023

  • Consultant Volunteer: CSE 291-J00 Deep Learning Lab (Computer Vision) at UC San Diego, Fall 2020

  • Teaching Assistant: CSE 152 Introduction to Computer Vision at UC San Diego, Fall 2018

Awards

Misc

  • I love powerlifting, tennis, golfing, and surfing in my free time.