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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="keywords" content="Construction robot, Reinforcement learning, Imitation learning, Dexterous manipulation, Robotic hand, Anthropomorphic">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Dexterous manipulation of construction tools using anthropomorphic robotic hand</title>
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<h1 class="title is-1 publication-title">Dexterous Manipulation of Construction Tools using Anthropomorphic Robotic Hand</h1>
<h3 class="title is-4 conference-authors">
<a target="_blank" href="https://www.sciencedirect.com/journal/automation-in-construction/"
style="color: #000000; text-decoration: none;">Automation in Construction</a> 156 (2023): 105133.
</h3>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://leihhhuang.github.io/">Lei Huang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://yesandy.github.io/andycai/">Weijia Cai</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/zihanzhuzz">Zihan Zhu</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=-p51P1kAAAAJ&hl=en">Zhengbo Zou</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of British Columbia</span>
</div>
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</div>
<!--/ Demo 2. -->
<h2 class="subtitle has-text-centered">
Dexterous robotic hand grasps a screwdriver,
relocates it to a box,
and releases palm for placement.
</h2>
</div>
</section>
<section class="section">
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<h2 class="title is-3">Abstract</h2>
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<p>
Emerging studies are utilizing reinforcement learning (RL) and imitation learning (IL)
to control large-scale robots in heavy construction tasks. There is limited attention
given to the automation of delicate tasks typically performed manually. This paper
proposes the control of an anthropomorphic robotic hand with a high degree of freedom
for the manipulation of construction tools in a learning-based approach. For
controlling the robotic hand, a simulation-based policy learning framework based on
pretraining policies through IL is presented, subsequently fine-tuning them for
construction tool manipulation using RL. In experimental trials, six policies are
trained for the robotic hand to grasp six different construction tools. The results
indicate that each of the learned policies enables the robotic hand to manipulate the
corresponding tool with an almost 100% success rate, demonstrating resilience when
confronted with tools of different scales. Additionally, the paper showcases the
potential for scaling up the fundamental policy for downstream applications.
</p>
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</div>
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<h2 class="title is-3">Overview</h2>
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alt="Interpolate start reference image." />
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<h2 class="title is-3">Video</h2>
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frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{huang2023105133,
author = {Huang, Lei and Cai, Weijia and Zhu, Zihan and Zou, Zhengbo},
title = {Dexterous manipulation of construction tools using anthropomorphic robotic hand},
journal = {Automation in Construction},
volume = {156},
pages = {105133},
year = {2023},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2023.105133},
url = {https://www.sciencedirect.com/science/article/pii/S092658052300393X},
}</code></pre>
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