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@ndennler
ndennler committed Mar 6, 2024
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Expand Up @@ -81,7 +81,7 @@ <h1 class="xtitle is-1 publication-title">Using Exploratory Search to Learn Repr
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://arxiv.org/pdf/<ARXIV PAPER ID>.pdf" target="_blank"
<a href="static/pdfs/CLEA.pdf" target="_blank"
class="external-link ">
<span class="icon">
<i class="fas fa-file-pdf"></i>
Expand All @@ -91,19 +91,19 @@ <h1 class="xtitle is-1 publication-title">Using Exploratory Search to Learn Repr
</span>

<!-- Supplementary PDF link -->
<span class="link-block">
<a href="static/pdfs/supplementary_material.pdf" target="_blank"
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<a href="static/pdfs/CLEA.pdf" target="_blank"
class="external-link ">
<span class="icon">
<i class="fas fa-file-pdf"></i>
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<span>Supplementary</span>
</a>
</span>
</span> -->

<!-- Github link -->
<span class="link-block">
<a href="https://github.com/YOUR REPO HERE" target="_blank"
<a href="https://github.com/ndennler/exploratory_action_representations" target="_blank"
class="external-link ">
<span class="icon">
<i class="fab fa-github"></i>
Expand All @@ -113,15 +113,16 @@ <h1 class="xtitle is-1 publication-title">Using Exploratory Search to Learn Repr
</span>

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<a href="https://arxiv.org/abs/<ARXIV PAPER ID>" target="_blank"
class="external-link ">
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<span>arXiv</span>
</a>
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Expand All @@ -131,7 +132,7 @@ <h1 class="xtitle is-1 publication-title">Using Exploratory Search to Learn Repr
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<h2 class="title is-3">Abstract</h2>
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin ullamcorper tellus sed ante aliquam tempus. Etiam porttitor urna feugiat nibh elementum, et tempor dolor mattis. Donec accumsan enim augue, a vulputate nisi sodales sit amet. Proin bibendum ex eget mauris cursus euismod nec et nibh. Maecenas ac gravida ante, nec cursus dui. Vivamus purus nibh, placerat ac purus eget, sagittis vestibulum metus. Sed vestibulum bibendum lectus gravida commodo. Pellentesque auctor leo vitae sagittis suscipit.
Robots that interact with humans must adapt to the different preferences of human users.
However, the time and effort needed for non-expert users to specify their preferences for a robot
are a barrier to effective robot adaptation.
Better representations of user preferences in the form of learned features have the potential to
facilitate robot adaptation.
In this work, we propose a method to learn representations using Contrastive Learning from Exploratory
Actions (CLEA) that leverages data automatically collected from an interactive signal design process
to better learn user preferences.
We show that using data collected automatically from the design process can aid with learning user
preferences compared to purely self-supervised learning.
</p>
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Expand All @@ -223,33 +232,29 @@ <h2 class="title is-3">Abstract</h2>


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<iframe src="https://youtu.be/IOJ1xDy0DEE" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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Expand All @@ -338,7 +340,13 @@ <h2 class="title">Poster</h2>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>BibTex Code Here</code></pre>
<pre><code>
@inproceedings{dennler2024exploratory,
title={Using Exploratory Search to Learn Represerations for Human Preferences},
author={Dennler, Nathaniel and Nikolaidisk, Stefanos and Matari{\'c}, Maja},
booktitle={Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction},
year={2024}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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