About

Hello! I am Jui-Te. I'm going to join The Robotics Institute at Carnegie Mellon University, as a student in MSR program.

Research Interest
Machine Learning:
Deep Reinforcement Learning, Cross-Modal Learning, Self-Supervised Learning
Robotic:
Control, Planning, Localization, Mapping
Perception:
Object Detection, Segmantation, Tracking
Projects:
DARPA Subterranean Challenge,
Maritime RobotX Challenge
Experience

Visiting Student at University of Washington
Department of Electrical and Computer Engineering
Information Processing Lab
Advised by Prof. Jenq-Neng Hwang
Seattle WA. Sep, 2021 ~ Feb, 2022

M.S. & B.S. at National Chiao Tung University
Institute of Electrical and Control Engineering
College of Electrical and Computer Engineering
Assistive Robotics Group
Advised by Prof. Hsueh-Cheng Wang
Hsinchu Taiwan. Sep, 2016 ~ Mar, 2022

Professional Skills
Programing:
C/C++, Python, Java
Deep Learning frame work:
Pytorch, Tensorflow
UI Design:
Qt, Android Studio
Robotics:
Robot Operating System
CV tools:
OpenCV, Open3D, Point Cloud Library
Mathematic tools:
Matlab, Scikit-Learn, Numpy, Scipy
Container:
Docker
Cloud Computing:
GCP, AWS, TWCC
Publication
Journal

  • Jui-Te, Huang, Chen-Lung Lu, Po-Kai Chang, Ching-I. Huang, Chao-Chun Hsu, Po-Jui Huang, and Hsueh-Cheng Wang, "Cross-Modal Contrastive Learning of Representations for Navigation Using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions." IEEE Robotics and Automation Letters, April 2021, [Paper] [Project]
  • Chen-Lung Lu*, Jui-Te Huang*, Ching-I Huang, Zi-Yan Liu, Chao-Chun Hsu, Yu-Yen Huang, Siao-Cing Huang, Po-Kai Chang, Zu Lin Ewe, Po-Jui Huang, Po-Lin Li, Bo-Hui Wang, Lai-Sum Yim, Sheng-Wei Huang, MingSian R. Bai, “A Heterogeneous Unmanned Ground Vehicle and Blimp Robot Team for Search and Rescue using Data-driven Autonomy and Communication-aware Navigation” Field Robotics - Special Issue: Advancements and lessons learned during Phase I & II of the DARPA Subterranean Challenge. 2021 (Accepted, *Equal Contribution) [Paper] [Project]
  • Chen-Lung Lu, Zi-Yan Liu, Jui-Te Huang, Ching-I Huang, Bo-Hui Wang, Yi Chen, Nien-Hsin Wu, Hsueh-Cheng Wang, Laura Giarré, Pei-Yi Kuo, "Assistive Navigation Using Deep Reinforcement Learning Guiding Robot With UWB/Voice Beacons and Semantic Feedbacks for Blind and Visually Impaired People" Frontier in Robotics and AI. 2021 [Paper] [Project]

  • Conference

  • Ni-Ching Lin, Yu-Chieh Hsiao, Yi-Wei Huang, Ching-Tung Hung, Tzu-Kuan Chuang, Pin-Wei Chen, Jui-Te Huang, Chao-Chun Hsu, Andrea Censi, Michael Benjamin, Chi-Fang Chen, Hsueh-Cheng Wang, "Duckiepond: An Open Education and Research Platform for a Fleet of Autonomous Maritime Vehicles" IEEE/RSJ International Conference on Intelligent Robots and Systems. 2019 [Paper] [Project]

  • Talks

  • Jui-Te Huang, Chao-Chun Hsu, Ching-Tung Hung, Andrea Censi, Michael Benjamin, Chi-Fang Chen, and Hsueh-Cheng Wang, “Duckiepond: An Initiative of Education and Research Environment for a Fleet of Autonomous Maritime Vehicles” Sixth Biennial Meeting of MOOS Development and Application Working Group, Cambridge MA, August, 2019 [Link] [Project]

  • Under Review

  • Yizhou Wang, Jiarui Cai, Jui-Te Huang, Yudong Li, Hung-Min Hsu, Hui Liu, Jenq-Neng Hwang, "Multi-Object Tracking with mmWave Radars: RadarMOT Framework for Autonomous Driving" IEEE Transactions on Multimedia. 2022
  • Projects
    Milimeter Wave Radar for Robot Navigation in Smoke

    IEEE Robotics and Automation Letters - 2021

    we propose the use of single-chip millimeter-wave (mmWave) radar, which is lightweight and inexpensive, for learning-based autonomous navigation. However, because mmWave radar signals are often noisy and sparse, we propose a cross-modal contrastive learning for representation (CM-CLR) method that maximizes the agreement between mmWave radar data and LiDAR data in the training stage to enable autonomous navigation using radar signal.

    Paper Video Github More...
    DARPA SubT Challenge - Urban Circuit

    Urban Circuit - Seatle - 2020

    Journal of Field Robotics - 2022

    The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments. I participated in the Urban Circuit as the software team lead and human supervisor. We build our robots to perform search and rescure mission in a unfinished nuclear power plant.

    Paper Video More...
    Assistive Navigation using Deep Reinforcement Learning Guiding Robot

    Frontier in Robotics and AI - 2021

    Facilitating navigation in pedestrian environments is critical for enabling people who are blind and visually impaired (BVI) to achieve independent mobility. A deep-reinforcement-learning-based assistive guiding robot with ultrawide-bandwidth (UWB) beacons that can navigate through routes with designated waypoints was designed in this study.

    Paper Web
    Human-to-Robot Handover via Socially-Aware End-to-End Grasping

    We present our socially-aware end-to-end grasping for human-to-robot handover. We first leverage existing end-to-end grasping as network backbone, and then finetune for non-invasive grasps and trajectories using sample efficient deep reinforcement learning. Comprehensive evaluations are carried out against various recent baselines using multi-stage hand and object prediction and subsequent planning.

    Preprint Video Web
    DARPA SubT Challenge - Tunnel Circuit

    Tunnel Circuit - Pittsburgh - 2019

    The DARPA Subterranean (SubT) Challenge aims to develop innovative technologies that would augment operations underground. The SubT Challenge will explore new approaches to rapidly map, navigate, search, and exploit complex underground environments. I participated in the Tunnel Circuit with my team and our robots to perform search and rescue mission in a mine tunnel.

    Arxiv More...
    Duckiepond

    IEEE/RSJ International Conference on Intelligent Robots and Systems - 2019

    Sixth Biennial Meeting of MOOS Development and Application Working Group, Cambridge MA, August, 2019

    This project is to help the people who are interested in marine robotics. We built a low-cost surface vehicle called duckieboat. Our vehicles are compatible with machine learning frame works. We demonstrate the implementation of classic autonomous navigation algorithms also tracking vehicles with deep learning computer vision with sensors on board.

    Paper Web Github More...
    Maritime RobotX Challenge

    Hawaii- 2018

    We participated in the 2018 Maritime RobotX Challenge and awarded number 5th at the final stage. We built a surface vehicle to solve multiple tasks including autonomous docking, navigation, obstacle avoidance, launch & recovery, scan code and acoustic pinging.

    Arxiv Web More...
    Contact Me
    Feel free to contact me