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            <p><b>CALL FOR PAPERS</b><br>
              <br>
              **Apologies for cross-posting**<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>June 17, 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>
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