Yulun Zhang

Yulun Zhang

Ph.D. Student

Carnegie Mellon University

Biography

I am Yulun Zhang (张宇伦). I am a first year Ph.D. student in Robotics Institute at the Carnegie Mellon University, advised by Jiaoyang Li. My research interests include human-robot collaboration, multi-robot coordination, evolutionary algorithms, and quality diversity optimization. As a long-term goal, my research focuses on bringing Quality Diversity Optimization and Evolutionary Optimization to Robotics, expanding their applicability and scalability.

Previously, I was a master/undergrad student majoring in Computer Science at the University of Southern California. I was working in Interactive and Collaborative Autonomous Robotics (ICAROS) lab at USC with Professor Stefanos Nikolaidis and Matt Fontaine. I was also working in Robotic Embedded Systems Laboratory (RESL) at USC with Dr. Ryan Julian and K.R. Zentner.

In addition, I was working in the USC Interaction Lab with Dr. Matt Rueben on Socially Assistive Robotics as well as Professor William Halfond’s group on record and replay tools for Android.

Here is my most recent CV (updated Jan 2023).

I am also enthusiastic about photography, especially scenery photography. Check out my portfolio and instagram for some of my works.

Interests
  • Quality Diversity Optimization
  • Evolutionary Computation
  • Human-Robot Interaction
  • Multi-Robot Coordination
  • Reinforcement Learning
Education

  • Ph.D. in Robotics

    Carnegie Mellon University

  • MSc in Computer Science, 2022

    University of Southern California

  • BSc in Computer Science, 2021

    University of Southern California

Projects

LDD Genie
A software tool for prosecution attorneys to perform logical document determination (LDD) electronically.
Slides
LDD Genie
pyribs
A bare-bones quality diversity (QD) optimization library. QD algorithm is a powerful alternative compared to single-objective optimization because it is able to search for a diverse set of solutions according to user-defined metrics. In this library we provide implementations of recent QD algorithms such as MAP-Elites and Covariance Matrix Adaptation MAP-Elites (CMA-ME).
Code Follow
pyribs
RERAN+
We found a time-sensitive issue of RERAN, a record and replay tool for Android published in 2013. In this project we propose a new scheduling mechanism for RERAN that resolve the time-sensitive issue.
Code
RERAN+

Publications

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Bryon Tjanaka, Matthew C. Fontaine, David H. Lee, Yulun Zhang, Nivedit Reddy Balam, Nathaniel Dennler, Sujay S. Garlanka, Nikitas Dimitri Klapsis, Stefanos Nikolaidis (2023). pyribs: A Bare-Bones Python Library for Quality Diversity Optimization. Preprint.

PDF Code Project

K.R. Zentner, Ujjwal Puri, Yulun Zhang, Ryan C. Julian, Gaurav S. Sukhatme (2022). Efficient Multi-Task Learning via Iterated Single-Task Transfer. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 23-27, 2022, Kyoto, Japan.

PDF DOI

Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, Stefanos Nikolaidis (2022). Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding. In GECCO ’22, July 9–13, 2022, Boston, MA, USA.

PDF Code Slides DOI

K.R. Zentner, Ryan C. Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme (2021). A Simple Approach to Continual Learning by Transferring Skill Parameters. Preprint.

PDF

Matthew Rueben, Mohammad Syed, Emily London, Mark Camarena, Eunsook Shin, Yulun Zhang, Timothy S. Wang, Thomas R. Groechel, Rhianna Lee, Maja J. Matarić (2021). Long-Term, in-the-Wild Study of Feedback about Speech Intelligibility for K-12 Students Attending Class via a Telepresence Robot. Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI ’21), October 18–22, 2021, Montréal, QC, Canada.

PDF DOI

Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang, Bryon Tjanaka, Stefanos Nikolaidis (2021). On the Importance of Environments in Human-Robot Coordination. In Robotics: Science and Systems 2021.

PDF Code Project Video DOI

K.R. Zentner, Ryan C. Julian, Ujjwal Puri, Yulun Zhang, Gaurav S. Sukhatme (2020). Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning. In NeurIPS 2020 Workshop: Challenges of Real World Reinforcement Learning.

PDF Video

Matthew Rueben, Thomas Groechel, Yulun Zhang, Gisele Ragusa, Maja J. Matarić (2020). Increasing Telepresence Robot Operator Awareness of Speaking Volume Appropriateness: Initial Model Development. Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction.

PDF DOI