Tongzhou Mu 木同舟

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

Policy Decorator: Model-Agnostic Online Refinement for Large Behavior Model
Xiu Yuan*^, Tongzhou Mu*, Yunaho Fang, Stone Tao, Michael Zhang, Hao Su,
Submitting to NeurIPS 2024
^ Xiu Yuan is an undergruduate student I advised

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
Submitting to NeurIPS 2024
[arXiv] [Code]

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]

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]

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

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]

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

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

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]

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]

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]

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

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]

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

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

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