Zhutian Yang ztyang {at} mit {dot} edu Hello! My name is Yang and I'm a PhD candidate at MIT working on robot learning and robot planning for long-horizon mobile manipulation problems so that we can have assistive robots helping with household chores. I'm co-advised by Leslie Pack Kaelbling and Tomás Lozano-Pérez in the Learning and Intelligent Systems group. I obtained master's degree in computer science at MIT and bachelor's degree in information engineering and media at NTU, Singapore.

Research

• Learn to plan with information provided by pre-trained vision-language models.
• Learn to manipulate objects that are partially observable using a mobile robot.
• Learn to solve continuous constraint satisfaction problems (e.g. packing without collisions).
• Learn to predict task plan feasibility from images of worlds with articulated and movable obstacles.
• Hierarchical planning and replanning in partially observable TAMP problems.

Publications

Compositional Diffusion-Based Continuous Constraint Solvers

Zhutian Yang, Jiayuan Mao, Yilun Du, Jiajun Wu, Joshua Brett Tenenbaum, Tomás Lozano-Pérez, and Leslie Pack Kaelbling

CoRL 2023

Paper  /  Project Page  /  Bibtex



Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning

Zhutian Yang, Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez, and Dieter Fox

RSS 2023

🔥 We also won Best Paper Runner-Up in CoRL 2022 Workshop on Learning, Perception, and Abstraction for Long-Horizon Planning

Paper  /  Project Page  /  Talk  /  MIT News  /  Tech Crunch  /  Poster (click to show) Poster (click to hide)   /  Bibtex



Let’s Handle It: Generalizable Manipulation of Articulated Objects

Zhutian Yang, and Aidan Curtis

ICRL 2022 Workshop on Generalizable Policy Learning in the Physical World

🔥 We won 2nd place in the ManiSkill Challenge 2022 Robotics Track

Paper  /  Poster (click to show) Poster (click to hide)



Flexibly Instructable Robots

Zhutian Yang, Patrick Henry Winston, and David Hsu

Undergraduate thesis work; Also appeared in Advances in Cognitive Systems 2019 and DSpace@MIT

Paper  /  Video Demo  /  Poster (click to show) Poster (click to hide)

Services

• Reviewed for ICLR 2022, IROS 2023, and RA-L 2023.
• Co-organized RSS 2023 Workshop on Learning for Task and Motion Planning.
• Serving as a student counselor in EECS Resources for Easing Friction and Stress since Summer 2020 and helped more than 10 graduate students through stressful situations such as changing advisors and family conflicts.
• Served as Teaching Assistant to MIT 6.036 Introduction to Machine Learning in Spring 2022.