Yunsheng Tian

Yunsheng Tian

PhD Candidate

MIT CSAIL

Yunsheng Tian is a fifth-year PhD student in the Computational Design & Fabrication Group at MIT CSAIL advised by Prof. Wojciech Matusik.

His current research centers on intelligent automation of manufacturing and design by leveraging tools from graphics, robotics, and machine learning. Specifically, he aims to develop:

  1. Integrated automation solutions for assembly planning and robotic execution;
  2. Adaptive experimental design algorithms and platforms to expedite scientific discovery or engineering optimization.

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 Autodesk Research, The University of Hong Kong and Microsoft Research Asia as a research intern.

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.
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.
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

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.
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 🧠 🤖

Neural Information Processing Systems (NeurIPS) 2023 RealML Workshop
(Best Student Paper)
Accelerated scientific discovery through active learning and autonomous robots.
Accelerated High-Entropy Alloys Discovery for Electrocatalysis via Robotic-Aided Active Learning

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

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

Neural Information Processing Systems (NeurIPS) 2020
Efficient multi-objective optimizer for expensive black-box functions.
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.
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Fluid Directed Rigid Body Control using Deep Reinforcement Learning 🤖 🎨

ACM Transactions on Graphics (SIGGRAPH) 2018
Deep RL can control high-dimensional fluid-rigid coupling dynamics.
Fluid Directed Rigid Body Control using Deep Reinforcement Learning

Projects

Autonomous Robotic Assembly of a Skateboard Truck 🤖

Autodesk Research Project 2023
Autonomous assembly of a mechanical skateboard truck with a flexible robotic system.
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.
 
 
 
 
 

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
 
 
 
 
 

Research Assistant

Media Computing Lab, Nankai University

Apr 2016 – Jun 2019 Tianjin, China

Services

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

Journal Reviewer: TVCG, TEVC, Soft Computing, TMLR

Invited Talks:

  • [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