Minjune Hwang

I am a graduate student in Computer Science at Stanford University, working on robot learning research with Prof. Fei-Fei Li, Prof. Jiajun Wu, and Dr. Ruohan Zhang in the Stanford Vision Lab.

Previously, I completed my undergraduate studies in Computer Science and Statistics from UC Berkeley, where I've done research in computer vision and convex optimization with Prof. Alexandre Bayen and Prof. Laurent El Ghaoui. I have spent time in Prof. David Wagner's group as well, working on adversarial machine learning

I was also fortunate to tackle exciting research problems in Amazon Robotics, Microsoft Research, and Apple, where I developed novel algorithms and practical systems to solve various problems in robotics.

I will be actively applying for PhD positions for fall 2024!

Email  /  Resume  /  GitHub  /  LinkedIn  /  Google Scholar

profile photo

The goal of my research is to develop systems and learning algorithms for efficient robot manipulation, from various kinds of data including human demonstrations, feedback, and environmental interactions. As of now, I am particularly interested in learning and effectively leveraging skills and representations for robot learning.

Specifically, my research focuses on methods for:

  • Learning policies and modeling reward from human demonstrations [1, 2] and feedback [3] for robot manipulation.
  • Using symbolic [1] and hierarchical [2, 3] representations to tackle long-horizon robotic tasks.
  • Developing frameworks (simulation environments [1] & brain-robot interface [2]) for efficient and robust robot learning.

Publications & Preprints (Highlighted Papers)
NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
Ruohan Zhang*, Sharon Lee*, Minjune Hwang*, Ayano Hiranaka*, Chen Wang, Wensi Ai, Jin Jie Ryan Tan, Shreya Gupta, Yilun Hao, Gabrael Levine, Ruohan Gao, Anthony Norcia, Li Fei-Fei, Jiajun Wu,
Conference on Robot Learning (CoRL), 2023
Cognitive Science & Robot Learning Workshop @ CoRL, 2023
paper / project page
(*: equal contribution)

Primitive Skill-Based Robot Learning from Human Evaluative Feedback
Ayano Hiranaka*, Minjune Hwang*, Sharon Lee, Chen Wang, Li Fei-Fei, Jiajun Wu, Ruohan Zhang,
International Conference on Intelligent Robots and Systems (IROS), 2023
arXiv / project page / poster
(*: equal contribution, alphabetically ordered)

Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Michael Lingelbach, Chengshu Li, Minjune Hwang, Andrey Kurenkov, Alan Lou, Roberto Martín-Martín, Ruohan Zhang, Li Fei-Fei, Jiajun Wu,
International Conference on Robotics and Automation (ICRA), 2023
arXiv / github

BEHAVIOR-1K: A Benchmark for Embodied AI with 1,000 Everyday Activities and Realistic Simulation
Chengshu Li, Cem Gokmen, Gabrael Levine, Roberto Martín-Martín, Sanjana Srivastava, Chen Wang, Josiah Wong, Ruohan Zhang, Michael Lingelbach, Jiankai Sun, Mona Anvari, Minjune Hwang, Manasi Sharma, Arman Aydin, Dhruva Bansal, Samuel Hunter, Kyu-Young Kim, Alan Lou, Caleb R Matthews, Ivan Villa-Renteria, Jerry Huayang Tang, Claire Tang, Fei Xia, Silvio Savarese, Hyowon Gweon, Karen Liu, Jiajun Wu, Li Fei-Fei,
Conference on Robot Learning (CoRL), 2022 (Best Paper Nominee)
paper / project page

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset
Fangyu Wu, Dequan Wang, Minjune Hwang, Chenhui Hao, Jiawei Lu, Jiamu Zhang, Christopher Chou, Trevor Darrell, Alexandre Bayen,
Arxiv Preprint, 2022
arXiv / project (github) page

Minority Reports Defense: Defending Against Adversarial Patches
Michael McCoyd, Won Park, Steven Chen, Neil Shah, Ryan Roggenkemper, Minjune Hwang, Jason Xinyu Liu, David Wagner,
Security in Machine Learning and its Applications (SiMLA), 2020 (Best Paper Award)
arXiv / github

Motion Planning in Understructured Road Environments with Stacked Reservation Grids
Fangyu Wu, Dequan Wang, Minjune Hwang, Chenhui Hao, Jiawei Lu, Trevor Darrell, Alexandre Bayen,
PAL @ ICRA, 2020

Text Analytics for Resilience-Enabled Extreme Events Reconnaissance
Alicia Yi-Ting Tsai*, Selim Günay*, Minjune Hwang*, Chenglong Li*, Pengyuan Zhai*, Laurent El Ghaoui, Khalid M.Mosalam,
AI + HADR @ NeurIPS, 2020
arXiv / project page / github
(*: equal contribution)

Building Resilience Through Structural Health Monitoring and Reconnaissance
Khalid M.Mosalam, Sifat Muin Selim Günay, Alicia Yi-Ting Tsai, Minjune Hwang, Chenglong Li, Jiang Lian Laurent El Ghaoui,
World Conference on Earthquake Engineering (WCEE), 2020
paper / github
  • Graduate Teaching Assistant: Stanford CS 231N [2023], Deep Learning for Computer Vision
  • Reader (Undergraduate Teaching Assistant): UC Berkeley EE 227BT [2020], Convex Optimization
  • Course Instructor: Ecole Bilingue de Berkeley [2019], Robotics & Programming (with Prof. Alex Bayen)
  • Undergraduate Lab Assistant: UC Berkeley CS 61A [2018], Structure and Interpretation of Computer Programs
Industry Experience

Website template from Jon Barron.