<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
</head>
<body>
<div class="moz-text-html" lang="x-unicode">
<div class="moz-text-html" lang="x-unicode">
<div class="moz-text-html" lang="x-unicode">
<div class="moz-text-html" lang="x-unicode">
<div class="moz-text-html" lang="x-unicode">
<div class="moz-text-html" lang="x-unicode">
<p><b>DEADLINE EXTENSION</b></p>
<p>**Apologies for cross-posting** </p>
We are happy to announce that the deadline for
submissions has been extended until <u><b>July 1</b></u>.</div>
<div class="moz-text-html" lang="x-unicode"><br>
</div>
<div class="moz-text-html" lang="x-unicode"><br>
<p><b>CALL FOR PAPERS</b><br>
<br>
The <b>full-day virtual</b> workshop:<br>
<br>
<b>Machine Learning for HRI: Bridging the Gap between
Action and Perception (ML-HRI)</b><br>
<br>
In conjunction with the <b>31st IEEE International
Conference on Robot and</b><b> Human Interactive
Communication (RO-MAN) - August 22, 2022 </b><br>
<br>
Webpage: <a href="https://ml-hri2022.ivai.onl/">https://ml-hri2022.ivai.onl/</a></p>
<p><br>
</p>
<p><b>I. Aim and Scope</b></p>
<p>A key factor for the acceptance of robots as partners
in complex and dynamic human-centered environments is
their ability to continuously adapt their behavior.
This includes learning the most appropriate behavior
for each encountered situation based on its specific
characteristics as perceived through the robots
senors. To determine the correct actions the robot has
to take into account prior experiences with the same
agents, their current emotional and mental states, as
well as their specific characteristics, e.g.
personalities and preferences. Since every encountered
situation is unique, the appropriate behavior cannot
be hard-coded in advance but must be learned over time
through interactions. Therefore, artificial agents
need to be able to learn continuously what behaviors
are most appropriate for certain situations and people
based on feedback and observations received from the
environment to enable more natural, enjoyful, and
effective interactions between humans and robots.<br>
<br>
This workshop aims to attract the latest research
studies and expertise in human-robot interaction and
machine learning at the intersection of rapidly
growing communities, including social and cognitive
robotics, machine learning, and artificial
intelligence, to present novel approaches aiming at
integrating and evaluating machine learning in HRI.
Furthermore, it will provide a venue to discuss the
limitations of the current approaches and future
directions towards creating robots that utilize
machine learning to improve their interaction with
humans.<br>
<br>
<b>II. Keynote Speakers and Panelists</b><br>
</p>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<ol>
<li><b>Dorsa Sadigh</b> – Stanford University – USA<br>
</li>
<li><b>Oya Celiktutan</b> – King's College London – UK</li>
<li><b>Sean Andrist </b>– Microsoft – USA</li>
<li><b>Stefan Wermter</b> – University of Hamburg –
Germany<br>
</li>
</ol>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<p><b>III. Submission</b><br>
</p>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<ol>
<li>For paper submission, use the following EasyChair
web link: <a
href="https://easychair.org/conferences/?conf=mlhri2022">Paper
Submission</a>.</li>
<li>Use the RO-MAN 2022 format: <a
href="http://www.smile.unina.it/ro-man2022/call-for-papers/">RO-MAN
Papers Templates</a>.</li>
<li>Submitted papers should be 4-6 pages for regular
papers and 2 pages for position papers.<br>
</li>
</ol>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<p> The primary list of topics covers the following
points (but not limited to):</p>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<ul>
<li>Autonomous robot behavior adaptation<br>
</li>
<li>Interactive learning approaches for HRI<br>
</li>
<li>Continual learning<br>
</li>
<li>Meta-learning<br>
</li>
<li>Transfer learning<br>
</li>
<li>Learning for multi-agent systems<br>
</li>
<li>User adaptation of interactive learning approaches<br>
</li>
<li>Architectures, frameworks, and tools for learning
in HRI<br>
</li>
<li>Metrics and evaluation criteria for learning
systems in HRI<br>
</li>
<li>Legal and ethical considerations for real-word
deployment of learning approaches</li>
</ul>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<p><b>IV. Important Dates</b><br>
</p>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<ol>
<li>Paper submission: <b><strike>June 17, 2021</strike></b><b>
July 1, 2022 (AoE)</b></li>
<li>Notification of acceptance: <b>August 1, 2022
(AoE)</b></li>
<li>Camera ready: <b>August 14, 2022 (AoE)</b><br>
</li>
<li>Workshop: <b>August 22, 2022</b><br>
</li>
</ol>
<blockquote>
<blockquote> </blockquote>
</blockquote>
<b>V. Organizers</b><br>
<blockquote> </blockquote>
<ol>
<li><b>Oliver Roesler</b> – IVAI – Germany<br>
</li>
<li><b>Elahe Bagheri</b> – IVAI – Germany</li>
<li><b>Amir Aly</b> – University of Plymouth – UK</li>
</ol>
</div>
</div>
</div>
</div>
</div>
</div>
</body>
</html>