Hung-Jui (Joe) Huang

I am a 3rd-year Ph.D. student at CMU RI, co-advised by Prof. Michael Kaess and Prof. Wenzhen Yuan. I am generally interested in robotics, computer vision, and machine learning. My research is about superhuman tactile sensing, including actively estimating physical properties and creating high-resolution 3D reconstructions.

I received my B.Sc. in EECS at MIT. During my undergrad, I worked with Prof. Gregory Stein and Prof. Nicholas Roy. After B.Sc., I worked at ISEE for two years with Dr. Chris Baker and Prof. Ying Nian Wu. In the past, I have worked on robot navigation and self-driving trucks.

Email / CV (Feb. 24) / Scholar / Github

CMU
Ph.D. in RI
Aug. 21 - Present

ISEE
Research Engineer
Aug. 19 - May 21

Nvidia
Intern
Jun. 18 - Aug. 18

MIT
B.Sc. in EECS
Sep. 14 - Jun. 19

Publications

	Kitchen Artist: Precise Control of Liquid Dispensing for Gourmet Plating Hung-Jui Huang, Jingyi Xiang, Wenzhen Yuan ICRA, 2024 video / paper / bibtex Our sauce plating robot can precisely control the thickness of squeezed liquids on a surface, even when dealing with unseen liquids.
	Estimating Properties of Solid Particles Inside Container Using Touch Sensing Xiaofeng Guo, Hung-Jui Huang, Wenzhen Yuan IROS, 2023 paper / bibtex Estimating solid particle properties (e.g., size and shape) inside a container using tactile and force-torque sensing.
	Understanding Dynamic Tactile Sensing for Liquid Property Estimation Hung-Jui Huang, Xiaofeng Guo, Wenzhen Yuan RSS, 2022 project page / video / paper / bibtex High-precision liquid viscosity and volume estimation using only tactile sensing. Our approach can even estimate sugar concentration within water based on slight viscosity variations.
	Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments Hung-Jui Huang, Kai-Chi Huang, Michal Čáp, Yibiao Zhao, Ying Nian Wu, Chris L. Baker ICRA, 2021 video / paper / bibtex A planning algorithm with guaranteed bounds on the probability of safety violation, which nonetheless achieve non-conservative performance. Tested on a self-driving truck in a real-world environment.
	Omnidirectional CNN for Visual Place Recognition and Navigation Tsun-Hsuan Wang, Hung-Jui Huang, Juan-Ting Lin, Chan-Wei Hu, Kuo-Hao Zeng, Min Sun (* indicates equal contribution) ICRA, 2018 project page / video / code / paper / bibtex O-CNN compares omnidirectional visual images to a database of images to determine the robot's location and helps navigation.

Teaching

I served as a teaching assistant for the following courses.

CMU 16-720 Computer Vision (Spring 2024)

MIT 6.141 Robotics: Science and Systems (Fall 2018)

Miscellanea

My chinese name is 黃泓睿 and it pronouced like "Huang-Hung-Ray". 😀
Outside of research, I read philosophy, meditate, and play video games.