Yunsheng Tian

Yunsheng Tian

PhD Candidate

MIT CSAIL

Yunsheng Tian is a final-year PhD student in the Computational Design & Fabrication Group at MIT CSAIL advised by Prof. Wojciech Matusik. His current research aims at:

  1. Building generalizable and adaptive robotic systems for complex industrial/household manipulation tasks, leveraging the power of physical simulation, planning, and learning;
  2. Developing data-efficient global optimizers involving multiple unknown objectives and constraints for general real-world applications.

Before coming to MIT, he obtained the bachelor’s degree from Nankai University, advised by Prof. Bo Ren and Prof. Ming-Ming Cheng. He had also worked at MIT-IBM Watson AI Lab, Autodesk Research, The University of Hong Kong and Microsoft Research Asia as a research intern.

News: We are organzing Workshop on Learning Robotic Assembly of Industrial and Everyday Objects at CoRL 2024!

Interests

  • Robotics 🤖
  • Machine Learning 🧠
  • Computer Graphics 🎨

Education

  • PhD in Computer Science, 2019 - Present

    Massachusetts Institute of Technology, MA, U.S.

  • BEng in Software Engineering, 2015 - 2019

    Nankai University, Tianjin, China

Publications

ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility 🤖 🧠

International Conference on Robotics and Automation (ICRA) 2024
Robotic assembly plan generation for complex and general-shaped assemblies.
PDF Code Dataset Project Poster Slides Video
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints 🧠

International Conference on Machine Learning (ICML) 2024
Efficient global optimizer for physical designs with unknown constraints.
PDF Code Poster
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly 🤖 🎨

ACM Transactions on Graphics (SIGGRAPH Asia) 2022
Physics makes motion planning for complex assemblies simple, fast, and generalizable.
PDF Code Dataset Project Video Talk MIT News
Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints 🧠 🎨

Computer Vision and Pattern Recognition (CVPR) 2022
Learning to assemble on a big CAD assembly dataset approaching human-level accuracy.
PDF Code Dataset
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots 🤖

Neural Information Processing Systems (NeurIPS) 2021
The first and largest benchmark for evolving soft robot morphology.
PDF Code Project Poster Video MIT News Wired IEEE Spectrum
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations 🧠

Yunsheng Tian*, Mina Konaković Luković*, Wojciech Matusik (* indicates equal contribution)
Neural Information Processing Systems (NeurIPS) 2020
Efficient multi-objective optimizer for expensive black-box functions.
PDF Code Poster Video Supplementary
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control 🧠 🤖

International Conference on Machine Learning (ICML) 2020
Efficient multi-objective RL for discovering all Pareto-optimal policies.
PDF Code Project Video Talk Supplementary
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Fluid Directed Rigid Body Control using Deep Reinforcement Learning 🤖 🎨

Yunsheng Tian*, Pingchuan Ma*, Zherong Pan, Bo Ren, Dinesh Manocha (* indicates equal contribution)
ACM Transactions on Graphics (SIGGRAPH) 2018
Deep RL can control high-dimensional fluid-rigid coupling dynamics.
PDF Project Video DOI Talk
Fluid Directed Rigid Body Control using Deep Reinforcement Learning

Workshop Papers

Towards Generalizable and Adaptive Multi-Part Robotic Assembly 🤖

Robotics: Science and Systems (RSS) 2024 GAS Workshop
A general dual-arm robotic system that end-to-end assembles multiple parts.
PDF Poster
Towards Generalizable and Adaptive Multi-Part Robotic Assembly

AutODEx: Automated Optimal Design of Experiments Platform with Data- and Time-Efficient Multi-Objective Optimization 🧠

Neural Information Processing Systems (NeurIPS) 2023 RealML Workshop
Easy-to-use experimental design platform based on asynchronous multi-objective BO.
PDF Project Poster Video
AutODEx: Automated Optimal Design of Experiments Platform with Data- and Time-Efficient Multi-Objective Optimization

Accelerated High-Entropy Alloys Discovery for Electrocatalysis via Robotic-Aided Active Learning 🧠 🤖

Zhichu Ren, Zhen Zhang, Yunsheng Tian, Ju Li
Neural Information Processing Systems (NeurIPS) 2023 RealML Workshop
(Best Student Paper)
Accelerated scientific discovery through active learning and autonomous robots.
PDF Video
Accelerated High-Entropy Alloys Discovery for Electrocatalysis via Robotic-Aided Active Learning

Projects

Autonomous Robotic Assembly of a Skateboard Truck 🤖

Yotto Koga, Hui Li, Xiang Zhang, Yunsheng Tian, Adam Arnold, James Emerick, Nic Carey, Srinidhi Srinivas, Nick Cote, Michael Koehle, Stefanie Pender, Noa Kaplan, Annabella Macaluso, Gadiel Sznaier Camps, Ozguc Capunaman, Gabrielle Patin, Hans Kellner, Sachin Chitta
Autodesk Research Project 2023
Autonomous assembly of a mechanical skateboard truck with a flexible robotic system.
Project Video
Autonomous Robotic Assembly of a Skateboard Truck

Experience

 
 
 
 
 

Research Intern

MIT-IBM Watson AI Lab

Jun 2024 – Aug 2024 Cambridge, MA, U.S.
Advised by Prof. Chuang Gan
 
 
 
 
 

Research Intern

AI Lab, Autodesk Research

May 2023 – Aug 2023 San Francisco, CA, U.S.
Advised by Dr. Jieliang Luo
 
 
 
 
 

Research Assistant

Computational Design & Fabrication Group, MIT CSAIL

Aug 2019 – Present Cambridge, MA, U.S.
Advised by Prof. Wojciech Matusik
 
 
 
 
 

Research Assistant

Department of Computer Science, The University of Hong Kong

Feb 2019 – May 2019 Hong Kong, China
Advised by Prof. Jia Pan
 
 
 
 
 

Research Intern

Visual Computing Group, Microsoft Research Asia

Jul 2018 – Sep 2018 Beijing, China
Advised by Mr. Zheng Zhang and Prof. Jifeng Dai
 
 
 
 
 

Research Assistant

Media Computing Lab, Nankai University

Apr 2016 – Jun 2019 Tianjin, China
Advised by Prof. Bo Ren and Prof. Ming-Ming Cheng

Services

Conference Reviewer: SIGGRAPH/SIGGRAPH Asia, CoRL, ICRA, IROS, ICLR, ICML, NeurIPS, AISTATS

Journal Reviewer: RA-L, TVCG, TEVC, Soft Computing, TMLR

Invited Talks:

  • [2024/07] DigitalFUTURES Workshop: “Towards Generalizable and Adaptive Robotic Assembly of Industrial and Everyday Objects”
  • [2023/11] Tsinghua University IIIS Seminar: “Towards Automated Planning and Execution for Generalizable and Complex Robotic Assembly”
  • [2023/08] Autodesk Research Connections: “Physics-Based Planning for Automated Robotic Assembly”
  • [2023/03] MIT Graphics Seminar: “Physics-Based Planning for Automating Contact-Rich Assembly”
  • [2021/04] MIT CSAIL Alliances Tech Workshop: “Automated Multi-Objective Optimal Experiment Design Platform”
  • [2020/11] Lam Research Seminar: “Multi-Objective Bayesian Optimization for Adaptive Experimental Design”

Teaching and Mentoring:

  • [Fall 2023] Teaching Assistant for MIT EECS Advanced Computer Graphics/Computational Design and Fabrication
  • [Fall 2020 - Present] Research Mentor of 8 students participating in MIT UROP (Undergraduate Research Opportunities Program)

Awards

  • [2023] Zetta Prize - Best Application of Artificial Intelligence in Industry: First place, given by MIT Machine Intelligence for Manufacturing & Operations Symposium
  • [2019] Outstanding Graduate: Top 4% among all students, given by Nankai University
  • [2016-2017] National Scholarship: Top 2% among all students, given by Ministry of Education of China

Contact

  • yunsheng [at] csail [dot] mit [dot] edu
  • 32 Vassar Street, 321, Cambridge, MA 02139