Haochen Shi

I'm a first-year Master student in Computer Science at Stanford University, where I work with Jiajun Wu and Huazhe Xu on robotics and computer vision. I worked with Michael Gleicher on robotics during my undergraduate at UW-Madison.

My research interests include robotics, computer vision, and potentially more! In my leisure time, I love playing tennis, Go, video games, and watching anime. I'm actively applying to PhD programs in Fall 2022!

Email  /  Google Scholar  /  Twitter  /  Github

profile photo
Research
CollisionIK: A per-instant pose optimization method for generating robot motions with environment collision avoidance
Daniel Rakita, Haochen Shi, Bilge Mutlu, Michael Gleicher
ICRA, 2021
[arXiv] [video]

In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment.

RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks
Haochen Shi*, Huazhe Xu*, Zhiao Huang, Yunzhu Li, Jiajun Wu
RSS, 2022
[arXiv] [project page]

Modeling and manipulating elasto-plastic objects are essential capabilities for robots to perform complex industrial and household interaction tasks (e.g., stuffing dumplings, rolling sushi, and making pottery). However, due to the high degree of freedom of elasto-plastic objects, significant challenges exist in virtually every aspect of the robotic manipulation pipeline, e.g., representing the states, modeling the dynamics, and synthesizing the control signals. We propose to tackle these challenges by employing a particle-based representation for elasto-plastic objects in a model-based planning framework.

Teaching

Template from Jon Barron's website