I am a computer science PhD student at the University of California, Berkeley, working on robotic manipulation and machine learning as part of Berkeley Artificial Intelligence Research (BAIR). I am interested in developing robotic systems for deployment in complex, unstructured environments, such as in surgical robotics and assitive home robotics.
I am fortunate to be advised by John Canny and Ken Goldberg. I am generously supported by a Graduate Fellowships for STEM Diversity (from 2015 to 2021), funded through the National Security Agency. I am originally from Albany, New York, and came to robotics at UC Berkeley through a long and winding road.
04/24/2018: I passed my PhD qualifying exam. Please see the bottom of this website for a transcript.
01/11/2018: Our paper on surgical debridement and calibration has been accepted to ICRA 2018.
08/02/2017: We wrote a BAIR Blog post about our work on minibatch Metropolis-Hastings.
Here is a talk I gave "at" the University of Toronto in March 2021, which provides a representative overview of my research.
Below, you can find my publications, as well as links to code, relevant blog posts,
and paper reviews. I strongly believe that researchers should make code publicly
available. Our code is usually on GitHub where you can file issue reports with questions.
I list papers under review (i.e., "preprints") first, followed by papers at accepted conferences, journals, or other venues in reverse chronological order.
If a paper is on arXiv, that's where you can find the latest version.
As is standard in our field, authors are ordered by contribution level, and asterisks (*) represent equality.
Life is short. :-) If you only have time to read one or two of the papers below, then I recommend our
recent ICRA 2021 paper "Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks"
or our RSS 2020 paper "VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation" (or its journal paper extension).
This is an extension of our RSS 2020 conference paper which presented VisuoSpatial Foresight (VSF). Here, we systematically explore different ways to improve different stages of the VSF pipeline, and find that adjusting the data generation enables better physical fabric folding.
We design a suite of tasks for benchmarking deformable object manipulation, including 1D cables, 2D fabrics, and 3D bags. We use Transporter Networks for learning how to manipulate some of these tasks, and for others, we design goal-conditioned variants.
We use dense object nets trained on simulated data and apply it to fabric manipluation tasks.
Since we train correspondences, we can take an action applied on a fabric, and "map" the corresponding action to a new fabric setup.
We have an IROS 2020 workshop paper that extends this idea to multi-modal distributions. [arXiv]
We propose a framework which uses a coarse controller in free space, and uses imitation learning to learn precise actions in regions that mandate the most accuracy. We test on the peg transfer task and show high success rates, and transferrability of the learned model across multiple surgical arms.
We design a custom fabric simulator, and script a corner-pulling demonstrator to train a fabric smoothing policy entirely in simulation using imitation learning. We transfer the policy to a physical da Vinci surgical robot.
We propose VisuoSpatial Foresight, an extension of visual foresight that additionally uses depth information, and use it for predicting what fabric observations (i.e., images) will look like given a series of actions.
We have since extended this paper into a journal submission (noted above).
We propose a system for robotic bed-making using a quarter-scale bed, which involves collecting real data and using color and depth information to detect blanket corners for pulling. This is applied on two mobile robots: the HSR and the Fetch.
We show how an ensemble of Q-networks can improve robustness of reinforcement learning. We use the ensemble to estimate variance. In simulated autonomous driving using TORCS, robust policies can better handle an adversary.
We show that we can estimate (simulated) grasp robustness using fully connected neural networks with grasp patches as input.
Coursework, Teaching, and Oral Exams
I have taken many graduate courses as part of the PhD program at UC Berkeley, typically in computer science (CS) but also in electrical engineering (EE) and statistics (STAT).
Some courses were new when I took them and had a "294-XYZ" number, before they took on a "regular" three-digit number.
I was also the GSI (i.e., Teaching Assistant) for the Deep Learning class in Fall 2016 and Spring 2019.
The course is now numbered CS 182/282A, where the 182 is for undergrads and the 282A is for graduate students.
CS 267, Applications of Parallel Computing
CS 280, Computer Vision
CS 281A, Statistical Learning Theory
CS 182/282A, Deep Neural Networks (GSI/TA twice)
CS 287, Advanced Robotics
CS 288, Natural Language Processing
CS 294-112, Deep Reinforcement Learning (now CS 285)
At the time I took it, UC Berkeley had an oral preliminary exam requirement for PhD students.
Here's the transcript of my prelims.
Nowadays, things might have changed since the number of AI PhD students has skyrocketed.
There is also a second oral exam, called the qualifying exam.
Here's the transcript of my qualifying exam.
I frequently blog about (mostly) technical topics. This blog is not affiliated with my university or employer, and I deliberately use a different domain name from this "eecs.berkeley.edu" page to reinforce this separation.
I also recommend checking the Berkeley AI Research blog. I was one of its maintainers for its first four years, and it's been great to see how much the blog has grown since then.
Quixotic though it may sound, I hope to use computer science and robotics to change the world for the better. If you have thoughts on how to do this, feel free to contact me.