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<!DOCTYPE html>
<html lang="en-US">
<head>
<meta charset="UTF-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Ching-I (Isabella) Huang</title>
<meta name="description" content="Isabella's CV" />
<link href="https://fonts.googleapis.com/css?family=Montserrat:400,700,200" rel="stylesheet" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css" />
<link href="css/aos.css" rel="stylesheet" />
<link href="css/bootstrap.min.css" rel="stylesheet" />
<link href="styles/main.css" rel="stylesheet" />
<script src="https://kit.fontawesome.com/5c88c749c7.js" crossorigin="anonymous"></script>
</head>
<body id="top">
<header>
<div class="profile-page sidebar-collapse">
<nav class="navbar navbar-expand-lg fixed-top navbar-transparent bg-primary" color-on-scroll="400">
<div class="container">
<div class="navbar-translate">
<a class="navbar-brand" href="#" rel="tooltip">Ching-I (Isabella) Huang</a>
<button
class="navbar-toggler navbar-toggler"
type="button" data-toggle="collapse"
data-target="#navigation"
aria-controls="navigation"
aria-expanded="false"
aria-label="Toggle navigation">
<span class="navbar-toggler-bar bar1"></span>
<span class="navbar-toggler-bar bar2"></span>
<span class="navbar-toggler-bar bar3"></span>
</button>
</div>
<div class="collapse navbar-collapse justify-content-end" id="navigation">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link smooth-scroll" href="#about">About</a>
</li>
<li class="nav-item">
<a class="nav-link smooth-scroll" href="#skill">Skills</a>
</li>
<li class="nav-item">
<a class="nav-link smooth-scroll" href="#publications">Publications
</a>
</li>
<!-- <li class="nav-item">
<a class="nav-link smooth-scroll" href="#projects">Projects
</a>
</li> -->
<li class="nav-item">
<a class="nav-link smooth-scroll" href="#contact">Contact</a>
</li>
</ul>
</div>
</div>
</nav>
</div>
</header>
<div class="page-content">
<div class="profile-page">
<div class="wrapper">
<div class="page-header page-header-small">
<div
class="page-header-image"
data-parallax="true"
style="background-image: url('images/background.png')">
</div>
<div class="container">
<div class="content-center">
<div class="h2 title">Ching-I (Isabella) Huang</div>
<p class="category text-white">Robotics, Robot Learning, VR</p>
</div>
</div>
<div class="section">
<div class="container">
<div class="button-container">
<a class="btn btn-default btn-round btn-lg btn-icon"
href="https://drive.google.com/drive/folders/164rY_U2C3lSZXpcbjHIc8gAm6KWuvI-u?usp=sharing"
rel="tooltip" title="Curriculum Vitae"><i class="ai ai-cv-square ai-2x"
style="padding-top: 0.35cm"></i></a>
<a class="btn btn-default btn-round btn-lg btn-icon"
href="https://www.linkedin.com/in/ching-i-h-6696a9220/" rel="tooltip" title="LinkedIn"><i
class="fa fa-linkedin"></i></a>
<a class="btn btn-default btn-round btn-lg btn-icon" href="https://github.com/cihuang123" rel="tooltip"
title="Github"><i class="fa fa-github"></i></a>
<a class="btn btn-default btn-round btn-lg btn-icon"
href="https://scholar.google.com/citations?hl=en&user=PQfRXVYAAAAJ" rel="tooltip"
title="Google Scholar">
<i class="ai ai-google-scholar ai-2x" style="padding-top: 0.35cm"></i>
</a>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="section" id="about">
<div class="container">
<div class="card" data-aos="fade-up" data-aos-offset="10">
<div class="row">
<div class="col-lg-4 col-md-12">
<div class="card-body">
<br />
<img src="images/isabella.jpg" alt="Image" width="160" style="
display: block;
margin-left: auto;
margin-right: auto;
border-radius: 20px;
" />
</div>
</div>
<div class="col-lg-8 col-md-12">
<div class="card-body">
<div class="h4 mt-0 title">About Ching-I</div>
<p>
Hello! I am <b>Ching-I (Isabella) Huang</b>, an Assistant Professor in the Department of
Power Mechanical Engineering (PME) at National Tsing Hua University, Taiwan.
My research focuses on <b>robot-learning approaches</b> and <b>virtual reality technologies</b> to
enhance <b>human-robot interaction</b>, with primary interests in <b>Human-Robot Teaming</b> and <b>VR-Assisted Teleoperation</b>.
</p>
<p>
I obtained my PhD from the Institute of Electrical and Control Engineering
at National Yang Ming Chiao Tung University (formerly National Chiao Tung University),
under the supervision of Nick Wang.
I have actively participated in significant competitions,
serving as the communication lead in the <b>DARPA SubT Challenge</b> in 2020
and the perception lead for the <b>RobotX Maritime Challenge</b> in 2022,
where our team secured 3rd place.
In 2023, I had the privilege of visiting Professor Lap-Fai (Craig) Yu's
research group at George Mason University,
collaborating on a project exploring optimal theories for gaming using augmented reality.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
<br><br><br><br>
<div class="section" id="skill">
<div class="container">
<div class="h4 text-center mb-4 title">Professional Skills</div>
<div
class="card"
data-aos="fade-up"
data-aos-anchor-placement="top-bottom"
>
<div class="card-body">
<div class="row">
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Programing:</strong>
</div>
<div class="col-sm-8">Python, C#</div>
</div>
</div>
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase"
>Deep Learning:</strong
>
</div>
<div class="col-sm-8">Pytorch</div>
</div>
</div>
</div>
<div class="row">
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Robotics:</strong>
</div>
<div class="col-sm-8">Robot Operating System</div>
</div>
</div>
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Development Platform:</strong>
</div>
<div class="col-sm-8">Unity </div>
</div>
</div>
</div>
<div class="row">
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">CV tools:</strong>
</div>
<div class="col-sm-8">
OpenCV
</div>
</div>
</div>
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Mathematic tools:</strong>
</div>
<div class="col-sm-8">Matlab, Numpy, Scipy</div>
</div>
</div>
</div>
<div class="row">
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Container:</strong>
</div>
<div class="col-sm-8">Docker</div>
</div>
</div>
<div class="col-lg-6 col-md-12">
<div class="mt-3">
<div class="col-sm-8">
<strong class="text-uppercase">Applications:</strong>
</div>
<div class="col-sm-8">Smart Healthcare, Search and Rescue</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<br><br><br><br>
<div class="section" id="publications">
<div class="container">
<div class="h4 text-center mb-4 title">Publications</div>
<div class="card" id="vr-navigation">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/vr-navigation.html">
<figure class="cc-effect">
<img src="project/vr-navigation/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
An Evaluation Framework of Human-Robot Teaming
for Navigation among Movable Obstacles via Virtual Reality-based Interactions</b>
</p>
<p class="category">IEEE Robotics and Automation Letters - 2024</p>
<p>
<b>Ching-I Huang</b>, Sun-Fu Chou, Li-Wei Liou, Nathan Alan Moy,
Chi-Ruei Wang, Hsueh-Cheng Wang, Charles Ahn, Chun-Ting Huang and Lap-Fai Yu
</p>
<p>
We present a framework that enables intuitive oversight of a robot team through immersive virtual
reality (VR) visualizations.
The framework simplifies the management of complex navigation among movable obstacles (NAMO) tasks,
such as search-and- rescue tasks.
Specifically, the framework integrates a simulation of the environment with robot sensor data in VR to
facilitate operator navigation,
enhance robot positioning, and greatly improve operator situational awareness.
The framework can also boost mission efficiency by seamlessly incorporating autonomous navigation
algorithms,
including NAMO algorithms, to reduce detours and operator workload.
</p>
<p>
The framework is effective for operating in both simulated and real scenarios and is thus ideal for
training or evaluating autonomous navigation algorithms.
To validate the framework, we conducted user studies (N = 53) on the basis of the DARPA SubT
Challenge’s search-and-rescue missions.
</p>
<a class="btn btn-primary" href="https://ieeexplore.ieee.org/abstract/document/10419887">
Paper</a>
<a class="btn btn-primary" href="project/vr-navigation/cite.html">
Cite</a>
<a class="btn btn-primary" href="https://vimeo.com/865390496">
Video</a>
<a class="btn btn-primary" href="https://github.com/cihuang123/framwork-unity">
Unity</a>
<a class="btn btn-primary" href="https://github.com/cihuang123/framework-ros">
ROS</a>
<!-- <a class="btn btn-primary" href="project/vr_navigation.html"
>More...</a> -->
</div>
</div>
</div>
</div>
<div class="card" id="fedhanet">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/fed-hanet.html">
<figure class="cc-effect">
<img src="project/fedhanet/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
Fed-hanet:Federated visual grasping learning for human robot handovers</b>
</p>
<p class="category">
IEEE Robotics and Automation Letters - 2023
</p>
<p>
<b>Ching-I Huang</b>, Yu-Yen Huang, Jie-Xin Liu, Yu-Ting Ko,
Hsueh-Cheng Wang, Kuang-Hsing Chiang and Lap-Fai Yu
</p>
<p>
Our Fed-HANet system has made significant strides in object-agnostic end-to-end planar grasping,
offering capabilities of up to six degrees of freedom (DoF)
while maintaining respect for the privacy and personal space of human counterparts.
Through experiments, our Fed-HANet system demonstrated accuracy levels comparable to centralized
non-privacy-preserving systems,
surpassing baseline methods reliant on fine-tuning. Additionally, we investigated the efficacy of a
depth-only approach,
comparing its performance to a state-of-the-art method, yet highlighting the superiority of RGB inputs
for enhanced grasp success.
Furthermore, we conducted a user study involving 12 participants to evaluate the practical
applicability of our proposed system within a robotic framework.
</p>
<a class="btn btn-primary" href="https://ieeexplore.ieee.org/abstract/document/10109097/">Paper
</a>
<a class="btn btn-primary" href="project/fedhanet/cite.html">
Cite</a>
<a class="btn btn-primary" href="https://vimeo.com/759768324">Video
</a>
<a class="btn btn-primary" href="https://github.com/ARG-NCTU/handover-system">System
</a>
<a class="btn btn-primary" href="https://github.com/ARG-NCTU/handover_grasping">Inference
</a>
<!-- <a class="btn btn-primary" href="project-fedhanet.html"
>More...</a
> -->
</div>
</div>
</div>
</div>
<div class="card" id="EfficientDets">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/efficientdet.html">
<figure class="cc-effect">
<img src="project/EfficientDets/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
Towards more efficient efficientdets and real-time marine debris detection</b>
</p>
<p class="category">
IEEE Robotics and Automation Letters - 2023
</p>
<p>
Federico Zocco, Tzu-Chieh Lin, <b>Ching-I Huang</b>,
Hsueh-Cheng Wang, Mohammad Omar Khyam and Lap-Fai Yu
</p>
<p>
In our pursuit of enhancing the efficiency of Autonomous Underwater Vehicle (AUV) vision systems for
real-time marine debris detection,
we have achieved significant advancements through the optimization of state-of-the-art object
detectors, specifically focusing on EfficientDets.
Our efforts have yielded notable improvements, including a 1.5% increase in Average Precision (AP) on
D0, 2.6% on D1, 1.2% on D2, and 1.3% on D3,
all achieved without incurring additional GPU latency.
Additionally, we have curated and publicly released a specialized dataset tailored for the detection
of in-water plastic bags and bottles.
Our enhanced EfficientDets models have been rigorously trained on this dataset, alongside integration
with two publicly available datasets
dedicated to marine debris detection.
</p>
<a class="btn btn-primary" href="https://ieeexplore.ieee.org/abstract/document/10044917">Paper
</a>
<a class="btn btn-primary" href="project/EfficientDets/cite.html">
Cite</a>
<a class="btn btn-primary" href="https://vimeo.com/794011415">Video
</a>
<!-- <a class="btn btn-primary" href="project-fedhanet.html"
>More...</a
> -->
</div>
</div>
</div>
</div>
<div class="card" id="subtUrban">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/SubT/index.html">
<figure class="cc-effect">
<img src="project/subt-urban/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
A Heterogeneous Unmanned Ground Vehicle and Blimp Robot
Team for Search and Rescue using Data-driven Autonomy and
Communication-aware Navigation</b>
</p>
<p></p>
<p class="category">
Journal of Field Robotics - 2022 <br />
DARPA SubT Challenge - Urban Circuit 2020
</p>
<p>
Chen-Lung Lu*, Jui-Te Huang*, <b>Ching-I Huang</b>, 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, and
Hsueh-Cheng Wang(*Equal Contribution)
</p>
<p>
We developed a heterogeneous team of unmanned ground vehicles and blimp robots for navigating unknown
subterranean environments in search and rescue missions.
Our system integrates novel millimeter-wave radar for perception and employs deep reinforcement
learning for cross-modal representations.
Navigation is facilitated by simulation-trained deep neural networks, ensuring effective collision
avoidance and goal-directed movement.
Our communication system combines mesh WiFi, XBee, and UWB modules, alongside deployable spherical
nodes and miniature cars for mobile communication.
Field tests were conducted to evaluate propagation and signal strength indices, addressing challenges
such as non-line-of-sight propagation and fading reception.
</p>
<a class="btn btn-primary" href="https://doi.org/10.55417/fr.2022020">Paper
</a>
<a class="btn btn-primary" href="project/subt-urban/cite.html">
Cite</a>
<a class="btn btn-primary" href="https://vimeo.com/501143422">Video
</a>
<a class="btn btn-primary" href="https://arg-nctu.github.io/SubT/">Team
</a>
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>More...</a
> -->
</div>
</div>
</div>
</div>
<div class="card" id="mmWave">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/deeprl-mmWave.html">
<figure class="cc-effect">
<img src="project/mmwave/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
Cross-Modal Contrastive Learning of Representations for
Navigation Using Lightweight, Low-Cost Millimeter Wave
Radar for Adverse Environmental Conditions</b>
</p>
<p class="category">
IEEE Robotics and Automation Letters - 2021
</p>
<p>
Jui-Te, Huang</b>, Chen-Lung Lu, Po-Kai Chang, <b>Ching-I
Huang</b>, Chao-Chun Hsu, Po-Jui Huang, and Hsueh-Cheng Wang
</p>
<p>
We advocate for utilizing single-chip millimeter-wave (mmWave) radar as robot perception, particularly
in challenging environments such as fog, smoke, or dust.
To address the inherent noise and sparsity of mmWave radar signals, we propose a cross-modal
contrastive learning for representation (CM-CLR) method.
This method aims to maximize the agreement between mmWave radar data and LiDAR data during training,
thereby enabling autonomous navigation using radar signals.
</p>
<a class="btn btn-primary" href="https://ieeexplore.ieee.org/document/9362209">Paper
</a>
<a class="btn btn-primary" href="project/mmwave/cite.html">
Cite</a>
<a class="btn btn-primary" href="https://vimeo.com/468839536">Video
</a>
<a class="btn btn-primary" href="https://github.com/cihuang123/radar-navigation">Github
</a>
<!-- <a class="btn btn-primary" href="project-mmwave.html"
>More...</a
> -->
</div>
</div>
</div>
</div>
<div class="card" id="blindguide">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/guiding_robot_uwb.html">
<figure class="cc-effect">
<img src="project/blind-guide/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>Assistive Navigation Using Deep Reinforcement Learning
Guiding Robot With UWB/Voice Beacons and Semantic
Feedbacks for Blind and Visually Impaired People</b>
</p>
<p class="category">Frontier in Robotics and AI - 2021</p>
<p>
Chen-Lung Lu, Zi-Yan Liu, Jui-Te Huang, <b>Ching-I
Huang</b>, Bo-Hui Wang, Yi Chen, Nien-Hsin Wu, Hsueh-Cheng Wang,
Laura Giarré, Pei-Yi Kuo
</p>
<p>
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.
</p>
<a class="btn btn-primary"
href="https://www.frontiersin.org/articles/10.3389/frobt.2021.654132/full">Paper
</a>
<a class="btn btn-primary" href="project/blind-guide/cite.html">
Cite</a>
<!-- <a class="btn btn-primary" href="https://arg-nctu.github.io/projects/guiding_robot_uwb.html">Web
</a> -->
</div>
</div>
</div>
</div>
</div>
</div>
<br><br><br><br>
<div class="section" id="conference">
<div class="container">
<div class="h4 text-center mb-4 title">Conference Paper</div>
<div class="card" id="wfh-vr">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/vr-robot-arm.html">
<figure class="cc-effect">
<img src="project/wfh-vr/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
Wfh-vr: Teleoperating a robot arm to set a dining table across the globe via virtual reality
</b>
</p>
<p class="category">
IEEE/RSJ International Conference on Intelligent Robots and
Systems - 2022
</p>
<p>
Lai Sum Yim, Quang TN Vo, <b>Ching-I Huang</b>, Chi-Ruei Wang,
Wren McQueary, Hsueh-Cheng Wang, Haikun Huang and Lap-Fai Yu
</p>
<p>
This paper introduces an accessible virtual reality-based teleoperation system for controlling a
low-cost robot arm (LoCoBot) using a consumer-grade VR device (Oculus Quest 2).
our Work-from-Home Virtual Reality (WFH-VR) system offers a seamless connection between the user
and the remote robot arm.
Virtual representations of the robot and objects are streamed to the VR interface, facilitating
manipulation tasks with or without network latency.
User studies demonstrate the system's effectiveness, enabling novices to perform dexterous
manipulation tasks beyond traditional keyboard controls.
</p>
<a class="btn btn-primary" href="https://ieeexplore.ieee.org/abstract/document/9981729">Paper
</a>
<a class="btn btn-primary" href="project/wfh-vr/cite.html">
Cite</a>
</div>
</div>
</div>
</div>
</div>
</div>
<br><br><br><br>
<div class="section" id="Workshop-Competitions">
<div class="container">
<div class="h4 text-center mb-4 title">Workshop & Competitions</div>
<div class="card" id="robotx2022">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="project/robotx2022/robotx2022.html">
<figure class="cc-effect">
<img src="project/robotx2022/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>A Learning- based modular heterogeneous usv and uav team in the maritime robotx 2022
competition</b>
</p>
<p class="category">
RobotX Maritime Challenge - 2022
</p>
<p>
Po-Jui Huang, <b>Ching-I Huang</b>, Sin-Kiat Lim, Po-Jui Huang, Ming-Fong Hsieh, Lai Sum Yim,
Yu-Ting Ko, Hao-Yang Hung, Yi Chen, Jie-Xin Liu,Li-Wei Liou, Sun-Fu Chou, Yi-Chen Teng, Kai-Rui
Weng, Wang-Chuan Lu, and Hsueh-Cheng Wang
</p>
<p>
Our exploration team, consisting of a USV and UAV with sensor arrays and autonomous modules, underwent refinement via hardware-in-the-loop (HIL) simulations.
Utilizing Gazebo from Virtual RobotX (VRX), we developed a perception dataset for deep reinforcement learning algorithms in maritime environments.
Results demonstrated the effectiveness of these algorithms, surpassing models trained solely with a UGV.
We enhanced fleet management with a VR interface and developed behavior trees for task execution.
Quantitative assessments were conducted in simulations before deployment on a WAM-V platform.
</p>
<a class="btn btn-primary" href="https://vimeo.com/758819296">Video
</a>
<a class="btn btn-primary" href="https://arg-nctu.github.io/robotx-2022/">
Team
</a>
<!-- <a class="btn btn-primary" href="project-robotx2022.html"
>More...</a
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<div class="card" id="moos2022">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="project/moos2022/moos2022.html">
<figure class="cc-effect">
<img src="project/moos2022/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>Duckiepond 2.0: an Education and Research Environment of Reinforcement Learning-based
Navigation and
Virtual Reality Human Interactions for a Heterogenous Maritime Fleet</b>
</p>
<p class="category">
Moos-DAWG'22 - 2022
</p>
<p>
<b>Ching-I Huang</b>, Hao-Yang Hung, Lai Sum Yim, Quang TN Vo,
Lap-Fai Yu and Hsueh-Cheng Wang
</p>
<p>
Duckiepond 2.0 expands on the Duckiepond education and research environment by introducing the
Duckieboat platform and simulation environments in Gazebo and Unity.
Duckieboat (DBT22) utilizes inflatable boats and outboard motors, featuring modular designs for
sensor towers, autonomy boxes, and communication modules.
These components can be deployed on various autonomous surface vehicles, enabling longer
communication ranges and hardware-in-the-loop (HIL) developments.
Duckiepond 2.0 inherits Duckietown's fleet management capabilities and supports MOOS-ROS bridge,
PyIvP, and non-ROS WebSocket for autonomy education.
Gazebo simulations aid in developing deep reinforcement learning algorithms for collision
avoidance, navigation, and docking.
Additionally, Duckiepond 2.0 introduces "supervised autonomy" through VR interactions, allowing a
human supervisor to control the maritime fleet using a consumer-grade VR device (Oculus Quest 2).
This setup facilitates search and rescue missions with virtual representations of Duckieboats and
surrounding objects for streamlined operation.
</p>
<!-- <a class="btn btn-primary" href="project-robotx2022.html"
>More...</a
>-->
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</div>
</div>
<div class="card" id="robotx2019">
<div class="row">
<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="project/robotx2019/robotx2019.html">
<figure class="cc-effect">
<img src="project/robotx2019/teaser.png" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>Long-term Operation, Low-Cost, Multi-Domain Collaborative Autonomous Maritime and Aerial
Platforms via Duckieboat and Duckiefloat
</b>
</p>
<p class="category">
RobotX Interactive Forum 2019 - 1st Place Spotlight Poster
</p>
<p>
<b>Ching-I Huang*</b>, Li-Wen Chiu*, Chao-Chun Hsu, Kuan-Lin Chen, Chih-Chuan Chang, Yi Chen, and
Hsueh-Cheng Wang
</p>
<p>
Duckieboat serves as the core of the Duckiepond development environment, facilitating collaborative maritime autonomy for both homogeneous and heterogeneous maritime vehicles.
Our research demonstrates multi-vehicle behaviors in track-and-trail tasks, advancements in obstacle avoidance utilizing classic and learning-based approaches.
Additionally, we enhance capabilities across multiple domains with Duckiefloat, an autonomous blimp developed for the DARPA Subterranean Challenge,
optimized for long-term observation tasks with minimal power consumption to excute long-period observation tasks.
</p>
<!-- <a class="btn btn-primary" href="project-robotx2022.html"
>More...</a
>-->
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</div>
</div>
</div>
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<div class="section" id="other publications">
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<div class="h4 text-center mb-4 title">Other Paper</div>
<div class="card" id="bionic">
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<div class="col-md-4" data-aos="fade-right" data-aos-offset="50" data-aos-duration="500">
<div class="card-body gallery">
<a href="https://arg-nctu.github.io/projects/vr-navigation.html">
<figure class="cc-effect">
<img src="project/bionic/system_boat.jpg" alt="Image" />
</figure>
</a>
</div>
</div>
<div class="col-md-8" data-aos="fade-left" data-aos-offset="50" data-aos-duration="500">
<div class="card-body">
<p>
<b>
Daytime radiative cooling of single-board computer in nearly enclosed unmanned surface vehicle</b>
</p>
<p class="category">Solar Energy Materials and Solar Cells - 2024</p>
<p>
Yen-Jen Chen, Sih-Wei Chang, Hsueh-Cheng Wang, Sin-Kiat Lim, <b>Ching-I Huang</b>, Fu-Hsiang Ko, Yu-Chieh Lo, Dehui Wan, Hsuen-Li Chen
</p>
<p>
"We present an efficient platform designed to facilitate the stable operation of single-board computers (SBCs) within the confines of an enclosed,
waterproof box aboard an unmanned surface vehicle (USV), even under direct sunlight.
The key innovation lies in the utilization of daytime radiative cooling (DRC) principles to effectively manage
the thermal conditions surrounding the central processing unit (CPU) of the SBCs. This is achieved through the deployment of metaparticles constructed
from a combination of various ceramic materials, which exhibit a high broadband emissivity exceeding 0.95 across the mid-wavelength infrared (MWIR)
to long-wavelength infrared (LWIR) spectra, while also boasting a solar reflectance rate exceeding 0.97.
</p>
<a class="btn btn-primary" href="https://www.sciencedirect.com/science/article/pii/S0927024824000357">
Paper</a>
<a class="btn btn-primary" href="project/bionic/cite.html"">
Cite</a>
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