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<!DOCTYPE HTML>
<html lang="en">
<head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Hao Zhang | 张昊</title>
<meta name="author" content="Hao Zhang">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
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<script>
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<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
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<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<td style="padding:2.5%;width:68%;vertical-align:middle">
<p style="text-align:center">
<name>Hao Zhang   |   张昊</name>
</p>
<p>
I am currently pursuing a Ph.D. in the Department of Automation at University of Science and Technology of China, advised by Prof. <a href="http://staff.ustc.edu.cn/~zkan/" target="_blank">Zhen Kan</a>.
</p>
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a>  / 
<a href="https://scholar.google.com/citations?user=rGpcMzYAAAAJ&hl=en">Google Scholar</a>  / 
<a href="https://github.com/Charlie0257">Github</a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/my/photo.jpg"><img style="width:75%;max-width:75%" alt="profile photo" src="images/my/photo.jpg" class="hoverZoomLink"></a>
<a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fcharlie0257.github.io&count_bg=%23E79717E9&title_bg=%23575454&icon=&icon_color=%23222121&title=visits&edge_flat=true"/></a>
</td>
</tr>
</tbody></table>
<table style="width:90%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<heading>Research Overview</heading>
<br>
</tr>
<p>
My current research interests include formal methods in robotics, reinforcement learning, and dexterous manipulation.
</p>
<br>
<br>
<br>
</tbody></table>
<!-- News -->
<table style="width:90%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<!-- <br> -->
<heading>News</heading>
<br>
<td style="padding:0px;width:100%;vertical-align:middle">
<p>
<li>[2024/06] 🎉 Two papers get accepted to IROS 2024.</li>
<li>[2024/05] 🎉 TALD is selected as <b><font color='red'>Best Paper Award</font></b> at ICAIS&ISAS 2024.</li>
<li>[2024/04] 🎉 TALD gets accepted to ICAIS&ISAS 2024.</li>
<li>[2023/06] 🎉 TRAPs gets accepted to IEEE Transactions on Cybernetics.</li>
<li>[2023/06] 🎉 T2TL gets accepted to IEEE RA-L.</li>
<li>[2022/05] 🎉 MQMT gets accepted to IEEE RA-L.</li>
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<heading>Publications</heading>
<p>
Representative works are <span class="highlight">highlighted</span>.
<p>
<br>
<tr onmouseout="malle_stop()" onmouseover="malle_start()" bgcolor="#ffffdc">
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/HyTL.png' width="190"></div>
<br>
<img src='images/HyTL.png' width="190">
</div>
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</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/document/10215053">
<papertitle>Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation</papertitle>
</a>
<br>
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Hao Wang</a:focus>,
<a:focus>Xiucai Huang</a:focus>,
<a:focus>Wenrui Chen</a:focus>,
<a:focus>Zhen Kan</a:focus>
<br>
<a href="https://ieeexplore.ieee.org/document/10802202">Paper</a>
/
<a href="https://sites.google.com/view/hytl-0257/">Website</a>
/
<a href="https://github.com/Charlie0257/HyTL">Code</a>
<br>
<em><strong>IROS (Conference), 2024</strong>, Accepted</em>
<p></p>
<p>
we develop a Temporal-Logicguided Hybrid policy framework (HyTL) which exploits three-level decision layers to facilitate robot learning.
</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/LEEPS.png' width="190"></div>
<br>
<img src='images/LEEPS.png' width="190">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/document/10215053">
<papertitle>LEEPS: Learning End-to-End Legged Perceptive Parkour Skills on Challenging Terrains</papertitle>
</a>
<br>
<a:focus>Tangyu Qian</a:focus>,
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Zhangli Zhou</a:focus>,
<a:focus>Hao Wang</a:focus>,
<a:focus>Mingyu Cai</a:focus>,
<a:focus>Zhen Kan</a:focus>
<br>
<a href="https://charlie0257.github.io/">Paper</a>
/
<a href="https://sites.google.com/view/leeps">Website</a>
/
<a href="https://github.com/P1terQ/LEEPS">Code</a>
<br>
<em><strong>IROS (Conference), 2024</strong>, Accepted</em>
<p></p>
<p>
We develop an End-to-End Legged Perceptive Parkour Skill Learning (LEEPS) framework to train quadruped robots to master parkour skills in complex environments.
</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/TALD.png' width="190"></div>
<br>
<img src='images/TALD.png' width="190">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/document/10215053">
<papertitle>Temporal Logic Guided Affordance Learning for Generalizable Dexterous Manipulation</papertitle>
</a>
<br>
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Hao Wang</a:focus>,
<a:focus>Tangyu Qian</a:focus>,
<a:focus>Zhen Kan</a:focus>
<br>
<a href="https://charlie0257.github.io/">Paper</a>
/
<a href="https://sites.google.com/view/tald-0257/">Website</a>
<br>
<em><strong>ICAIS&ISAS (Conference), 2024</strong>, Accepted</em>
<p></p>
<p>
We develop a temporal logic guided affordance learning framework for generalizable dexterous manipulations (TALD), which exploits affordance learning and task semantics to further improve generalization performance.
</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/TARP.png' width="190"></div>
<br>
<img src='images/TARP.png' width="190">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/document/10215053">
<papertitle>Task-Driven Reinforcement Learning with Action Primitives for Long-Horizon Manipulation Skills</papertitle>
</a>
<br>
<a:focus>Hao Wang</a:focus>,
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Lin Li</a:focus>
<a:focus>Zhen Kan</a:focus>
<a:focus>Yongduan Song</a:focus>
<br>
<a href="https://ieeexplore.ieee.org/document/10215053">Paper</a>
<br>
<em><strong>IEEE Transactions on Cybernetics (Journal), 2023</strong>, Accepted</em>
<p></p>
<p>
We develop Task-driven Reinforcement learning with Action Primitives (TRAPs), a new manipulation skill learning framework that augments standard reinforcement learning algorithms with formal methods and parameterized action space.
</p>
</td>
</tr>
<tr onmouseout="malle_stop()" onmouseover="malle_start()" bgcolor="#ffffdc">
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/T2TL.png' width="190"></div>
<br>
<img src='images/T2TL.png' width="190">
</div>
<script type="text/javascript">
function malle_start() {
document.getElementById('malle_image').style.opacity = "1";
}
function malle_stop() {
document.getElementById('malle_image').style.opacity = "0";
}
malle_stop()
</script>
</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://arxiv.org/abs/2209.13220">
<papertitle>Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning</papertitle>
</a>
<br>
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Hao Wang</a:focus>,
<a:focus>Zhen Kan</a:focus>
<br>
<a href="https://arxiv.org/abs/2209.13220">Paper</a>
/
<a href="https://github.com/Charlie0257/T2TL">Code</a>
<br>
<em><strong>IEEE RA-L (Journal), 2023</strong>, Accepted</em>
<p></p>
<p>
We develop a Double-Transformer-guided Temporal Logic framework (T2TL) that exploits the structural feature of Transformer twice.
</p>
</td>
</tr>
<tr>
<td style="padding:20px;width:30%;vertical-align:middle">
<div class="one">
<div class="two" id='malle_image'>
<br>
<img src='images/MQMT.png' width="190"></div>
<br>
<img src='images/MQMT.png' width="190">
</div>
<script type="text/javascript">
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</td>
<td style="padding:20px;width:70%;vertical-align:middle">
<a href="https://ieeexplore.ieee.org/document/9803859">
<papertitle>Temporal Logic Guided Meta Q-Learning of Multiple Tasks</papertitle>
</a>
<br>
<a:focus><strong>Hao Zhang</strong></a:focus>,
<a:focus>Zhen Kan</a:focus>
<br>
<a href="https://ieeexplore.ieee.org/document/9803859">Paper</a>
<br>
<em><strong>IEEE RA-L (Journal), 2022</strong>, Accepted</em>
<p></p>
<p>
We develop a meta Q-learning of multi-task (MQMT) framework where the robot effectively learns a meta model from a diverse set of training tasks and then generalizes the learned model to a new set of tasks.
</p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:0px">
<br>
<p style="text-align:right;font-size:small;">
This template is a modification to <a href="https://jonbarron.info/">Jon Barron's website</a>.
</p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</table>
</body>
</html>