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!
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.
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.