Hung-Jui (Joe) Huang

I am a 5th-year Ph.D. student at CMU RI, co-advised by Prof. Michael Kaess and Prof. Wenzhen Yuan. My research focuses on superhuman tactile sensing and tactile-driven dexterous manipulation. On the tactile sensing side, I use touch to estimate physical properties, track object pose during contact, and create high-resolution 3D reconstructions.

I recently completed two research internships at Google DeepMind, working on multi-modal sensing for vision-language-action (VLA) models and real-world reinforcement learning, applied to dexterous and bimanual manipulation, where robots coordinate two hands for contact-rich tasks.

Before CMU, I received my B.Sc. in EECS from MIT and spent two years at ISEE working on self-driving trucks. I am a Presidential Fellow at CMU.

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Experience

DeepMindDeepMind
Student Researcher ×2
Jul. 25 – Jan. 26
Mar. 26 – Aug. 26
CMUCMU
Ph.D. in Robotics
Aug. 21 – Present
ISEEISEE
Research Engineer
Aug. 19 – May 21
NVIDIANVIDIA
Intern
Jun. 18 – Aug. 18
MITMIT
B.Sc. in EECS
Sep. 14 – Jun. 19

Publications

GelSLAM: A Real-Time, High-Fidelity, and Robust 3D Tactile SLAM System
T-RO, 2026

Extremely high-fidelity, real-time 3D reconstruction using only tactile input, robust across tens of thousands of frames and hundreds of contact breaks.

TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches
Langzhe Gu, Hung-Jui Huang*, Mohamad Qadri*, Michael Kaess, Wenzhen Yuan (* equal contribution)
ECCV, 2026

Reconstructing 3D objects with fine geometric details from sparse robot touches, guided by diffusion priors.

GelBelt: A Vision-based Tactile Sensor for Continuous Sensing of Large Surfaces
Amin Mirzaee, Hung-Jui Huang, Wenzhen Yuan
RA-L, 2024

Our tactile sensor is designed for efficient, continuous scanning over large surfaces.

NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose Tracking with Vision-based Tactile Sensors
Hung-Jui Huang, Michael Kaess, Wenzhen Yuan
RA-L, 2024
Best Poster Award, ICRA 2025 Dexterity in Multi-Fingered Hands Workshop

Real-time, robust, and accurate tactile-based tracking for novel objects, including low-textured ones like ping pong balls and eggs. Useful for manipulation, in-hand manipulation, and 3D reconstruction tasks.

FusionSense: Bridging Common Sense, Vision, and Touch for Robust Sparse-View Reconstruction
ICRA, 2025

Robot reconstructing visually and geometrically accurate surroundings with sparse visual and tactile data.

Pancake robot
An Intelligent Robotic System for Perceptive Pancake Batter Stirring and Precise Pouring
IROS, 2024
IROS Best Entertainment and Amusement Papers Finalist

Our pancake preparation robot integrates advanced sensory and control algorithms to mix batter with precise uniformity, estimate its properties, and pour it into specified shapes.

Kitchen Artist: Precise Control of Liquid Dispensing for Gourmet Plating
Hung-Jui Huang, Jingyi Xiang, Wenzhen Yuan
ICRA, 2024

Our sauce plating robot can precisely control the thickness of squeezed liquids on a surface, even when dealing with unseen liquids.

Solid estimation
Estimating Properties of Solid Particles Inside Container Using Touch Sensing
Xiaofeng Guo, Hung-Jui Huang, Wenzhen Yuan
IROS, 2023

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

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
ICRA, 2021

A planning algorithm with guaranteed bounds on the probability of safety violation, which nonetheless achieves non-conservative performance. Tested on a self-driving truck in a real-world environment.

Omnidirectional CNN
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 (* equal contribution)
ICRA, 2018

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 pronounced like "Huang-Hung-Ray". 😀
Outside of research, I read philosophy, meditate, and play video games.