Tongzhou Mu 木同舟
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Ph.D. candidate, |
About me
I am a Ph.D. candidate in the Department of Computer Science and Engineering at the University of California San Diego, and I am fortunate to be advised by Prof. Hao Su. My long-term research goal is to build a decision-making framework with strong generalizability. Specifically, I am interested in Reinforcement Learning / Imitation Learning, Concept Discovery and Reasoning, and Robotics / Embodied AI.
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 Microsoft Research Asia, Intel AI, Wormpex AI , and Amazon Alexa AI.
I am the major contributor of the ManiSkill benchmark, and the lead organzier of the ManiSkill Challenge 2021 and the Generalizable Policy Learning in the Physical World Workshop.
Research interests
My research interests include
Reinforcement Learning / Imitation Learning
Sturctured Policies and Neural Programs
Learning from Demonstrations
Curriculum in RL
Concept Discovery
Emerged Concepts from Interactions
Reasoning over Concepts
Robotics / Embodied AI
Planning and Control for Manipulation
Building Tasks in Simulation
Publications & Preprints
Papers sorted by years. Representative papers are highlighted.
2023
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Learning Reusable Dense Rewards for Multi-Stage Tasks |
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On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline |
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Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization |
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Boosting Reinforcement Learning and Planning with Demonstrations: A Survey |
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ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills |
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Close the Optical Sensing Domain Gap by Physics-Grounded Active Stereo Sensor Simulation |
2022
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Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning |
2021
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ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations |
2020
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Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals |
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State Alignment-based Imitation Learning |
Before 2019
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Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning |
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Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization |
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Adaptive Variance Reducing for Stochastic Gradient Descent |
Talks
Boosting Reinforcement Learning and Planning with Demonstrations
Ph.D. Research Exam at UC San Diego, Jan 2023
On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
Pre-training Robot Learning Workshop at CoRL 2022, Dec 2022
Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
UC Berkeley Robot Learning Lab, Nov 2021
Stanford Vision and Learning Lab, Dec 2021
Task-driven Entity Abstraction from Visual Observations
Qualcomm AI Lab, Mar 2020
Professional Services
Workshop Organizer: ICLR 2022 Workshop “Generalizable Policy Learning in the Physical World” (Lead Organzier)
Tutorial Organizer: CVPR 2022 Tutorial “Building and Working in Environments for Embodied AI”
Challenge Organizer:
SAPIEN ManiSkill Challenge 2021 (Lead Organzier)
Conference Reviewer: ICLR, NeurIPS, ICRA, IROS, ICCV, AAAI
Journal Reviewer: RA-L
Teaching
Consultant Volunteer: CSE291-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
ACM-ICPC (International Collegiate Programming Contest) Asia Regional Contest Gold Medal, 2015
China Computer Federation Elite Collegiate Award (top 108 in China), 2016
Award of Excellence for Stars of Tomorrow Internship Program, Microsoft Research Asia, 2017