Hi! I am a third-year PhD student at USC, advised by Professors Jesse Thomason and Erdem Biyik at USC and Joseph J. Lim at KAIST on deep reinforcement learning for robotics. I completed my undergrad at UC Berkeley, where I worked with Professors Sergey Levine and Dinesh Jayaraman. I have interned at NAVER AI Labs and Horizon Robotics.
I am currently interning this summer at AWS doing research in Rasool Fakoor’s team.
I am interested in improving generalization and sample-efficiency of reinforcement learning algorithms by injecting inductive biases (e.g., programs), incorporating large offline datasets (e.g., through offline RL), and guiding agents with external knowledge (e.g., large language models).
CS PhD, 2020 - ?
USC
BA in Computer Science, 2016 - 2020
UC Berkeley
[5/31/23] Started an internship at AWS research this summer!
[11/22/22] SPRINT is accepted as a spotlight talk at CoRL 2022 LangRob Workshop!
[4/22/22] I have received the ICLR 2022 highlighted reviewer award (top 8%)!
[12/15/21] Gave a talk at the AIPlans Workshop at NeurIPS 2021!
Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks. Prior works have defined pre-training tasks via natural language instructions, but doing so requires tedious human annotation of hundreds of thousands of instructions. Thus, we propose SPRINT, a scalable offline policy pre-training approach which substantially reduces the human effort needed for pre-training a diverse set of skills. Our method uses two core ideas to automatically expand a base set of pre-training tasks: instruction relabeling via large language models and cross-trajectory skill chaining through offline reinforcement learning. As a result, SPRINT pre-training equips robots with a much richer repertoire of skills. Experimental results in a household simulator and on a real robot kitchen manipulation task show that SPRINT leads to substantially faster learning of new long-horizon tasks than previous pre-training approaches.
CSCI 566: Deep learning (GSI @ USC, Spring 2023): · Held office hours, advised 4 deep learning project teams, configured course to use Gradescope
CSCI 360: Intro to AI (GSI @ USC, Spring 2022): · Held in-class discussion sections, office hours, created new written homework assignments, wrote test questions, and reconfigured course grading structure to use Gradescope
CSCI 566: Deep Learning (GSI @ USC, Fall 2020): · Gave 2 lectures (RL for Robotics), advised 6 deep learning project teams, and held office hours
CS 188: Intro to AI (undergraduate GSI @ UC Berkeley, Fall 2019): · Lead a discussion section and held office hours · Received a teaching rating of 4.75/5, 0.42 above the department average
CS 170: Intro to CS Theory (course reader @ UC Berkeley, Spring 2019): · Held office hours and volunteered to write problems for and help run extra sections on difficult material**
Reviewing: · Serving/Served as a reviewer for ICML 2023, ICLR 2023, CoRL 2022, ICML 2022, ICLR 2022 (highlighted reviewer award, top 8%), NeurIPS 2021 (outstanding reviewer award, top 8%), CoRL 2021, ICLR 2021, ICLR SSL-RL Workshop 2021, IEEE ITSC 2019
Irvine ML Organization: · Serving as a principal advisor for Irvine ML Org, which teaches high schoolers about ML in Irvine, CA