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<!DOCTYPE HTML>
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<html>
<head>
<title>Data Meets Physics in Robotics</title>
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<span class="image fit"><img src="images/icra/ICRA-VIE26-logo.png" alt="ICRA 2026 Logo" style="width: 100%; height: auto;" /></span>
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</div>
</div>
<div style="text-align: center; margin-top: 1em;">
<h1>1st Workshop on Data Meets Physics:<br/>
Combining Data-Driven and Physics-Based Methods in Robotics</h1>
</div>
<p>2026 IEEE International Conference on Robotics & Automation (ICRA)<br/>
<a href="#" class="icon solid solo fa-calendar-alt"><span class="label">Calender</span></a> 1st June 2026 <br/>
<a href="#" class="icon solid solo fa-globe-europe"><span class="label">Location</span></a> Vienna, Austria</p>
</p>
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<!-- Nav -->
<nav id="nav">
<ul>
<li><a href="#intro" class="active">About</a></li>
<li><a href="#first">Speakers</a></li>
<li><a href="#second">Program</a></li>
<li><a href="#cta">Call for Papers</a></li>
<li><a href="#org">Organizer</a></li>
</ul>
</nav>
<!-- Main -->
<div id="main">
<!-- About-->
<section id="intro" class="main">
<header class="major">
<h2>About the workshop</h2>
</header>
<!-- TL;DR Section -->
<div class="box alt" style="background: linear-gradient(135deg, #e3f2fd 0%, #f3e5f5 100%); color: #333333; padding: 2em; border-radius: 10px; margin-bottom: 2em; box-shadow: 0 10px 30px rgba(0,0,0,0.1);">
<div style="display: flex; align-items: flex-start; gap: 1em;">
<div style="flex-shrink: 0;">
<h3 style="color: #4a6cf7; margin: 0; font-weight: bold; text-shadow: none;">
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TL;DR
</h3>
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<div style="flex: 1; padding-top: 0.2em;">
<div style="margin-bottom: 0.8em; color: #333333; display: flex; align-items: flex-start;">
<div style="width: 1.5em; flex-shrink: 0; text-align: center;">
<i class="icon solid fa-robot" style="color: #4a6cf7; font-size: 1.1em;"></i>
</div>
<div style="flex: 1; margin-left: 0.5em;">
<strong style="color: #2d4a96;">The Challenge:</strong> Pure machine learning struggles to generalize with limited training data
</div>
</div>
<div style="margin-left: 0em; margin-bottom: 0.8em; color: #333333; display: flex; align-items: flex-start;">
<div style="width: 1.5em; flex-shrink: 0; text-align: center;">
<i class="icon solid fa-atom" style="color: #4a6cf7; font-size: 1.1em;"></i>
</div>
<div style="flex: 1; margin-left: 0.5em;">
<strong style="color: #2d4a96;">The Opportunity:</strong> Physics-aware learning to integrate data & prior physics knowledge
</div>
</div>
<div style="margin-left: 0em; margin-bottom: 0.8em; color: #333333; display: flex; align-items: flex-start;">
<div style="width: 1.5em; flex-shrink: 0; text-align: center;">
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</div>
<div style="flex: 1; margin-left: 0.5em;">
<strong style="color: #2d4a96;">The Workshop:</strong> A forum to advance physics-aware learning in robotics
</div>
</div>
<div style="margin-left: 0em; margin-bottom: 0; color: #333333; display: flex; align-items: flex-start;">
<div style="width: 1.5em; flex-shrink: 0; text-align: center;">
<i class="icon solid fa-calendar-check" style="color: #4a6cf7; font-size: 1.1em;"></i>
</div>
<div style="flex: 1; margin-left: 0.5em;">
<strong style="color: #2d4a96;">The Format:</strong> Invited talks, round table & poster session for contributed papers
</div>
</div>
</div>
</div>
</div>
<p><span class="image"><img src="images/diagram_from_model_to_data.png" style="max-width: 100%; width: 100%; height: auto;" alt="" /></span>
<p>
Machine learning is reshaping robotics, enabling new capabilities in modeling, decision-making, perception and control. However, purely data-driven methods often struggle to deliver generalizable performance in safety-critical applications, where limited training data is available. To address this, recent research has been exploring ways to embed physics-based and classical engineering principles into learning architectures, enhancing interpretability, generalization and sample efficiency. This has led to <em>physics-driven</em> learning paradigms that merge the strengths of data-driven and model-based approaches.
</p>
<p>
The workshop <em>Data Meets Physics</em> at ICRA 2026 will bring together researchers from robotics, machine learning, and control to examine the opportunities and challenges of combining physics-based and data-driven methods. Discussions will focus on embedding domain knowledge into learning frameworks, moving beyond black-box models toward hybrid approaches that couple data with physical insight. Through invited talks, a round table, and a poster session, the workshop will foster knowledge exchange among professors, early-career researchers, and practitioners, encouraging collaboration and innovation in this rapidly-evolving field.
</p>
</section>
<section>
</section>
</section>
<!-- Speakers -->
<section id="first" class="main">
<header class="major">
<h2>Confirmed Speakers</h2>
</header>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Ken-Goldberg-w-Stone-Post-square.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Cosimo.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Alice_Plebe.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4" , align="center">
<b><a href="https://goldberg.berkeley.edu/">Ken Goldberg</a></b>
<h5>Full Professor</h6>
<h5>University of California, Berkeley</h6>
</div>
<div class="col-4" , align="center">
<b><a href="https://cosimodellasantina.eu/">Cosimo Della Santina</a></b>
<h5>Associate Professor</h6>
<h5>Delft University of Technology</h6>
</div>
<div class="col-4" , align="center">
<b><a href="https://profiles.ucl.ac.uk/99281-alice-plebe">Alice Plebe</a></b>
<h5>Post-doctoral Researcher</h6>
<h5>University College London </h6>
</div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Findeisen_Rolf_415x415.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Thomas_Beckers.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Madhur_Behl.jpg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4" , align="center">
<b><a href="https://www.etit.tu-darmstadt.de/fachbereich/professuren_etit/etit_prof_details_104640.de.jsp">Rolf Findeisen</a></b>
<h5>Full Professor</h6>
<h5>TU Darmstadt</h6>
</div>
<div class="col-4" , align="center">
<b><a href="https://www.tbeckers.com/team">Thomas Beckers</a></b>
<h5>Assistant Professor</h5>
<h5>Vanderbilt University</h5>
</div>
<div class="col-4" , align="center">
<b><a href="https://engineering.virginia.edu/faculty/madhur-behl">Madhur Behl</a></b>
<h5>Associate Professor</h5>
<h5>University of Virginia</h5>
</div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4"><span class="image fit"><img src="images/workshop_speakers/Ines_Sorrentino.jpeg"
alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4" , align="center">
<b><a href="https://www.iit.it/people-details/-/people/ines-sorrentino">Ines Sorrentino</a></b>
<h5>Post-doctoral Researcher</h6>
<h5>Istituto Italiano di Tecnologia</h6>
</div>
</div>
</div>
</section>
<!-- Program -->
<section id="second" class="main">
<header class="major">
<h2>Program</h2>
</header>
<p>
The workshop will involve <strong class="highlight">seven invited talks</strong> from leading experts and early-career researchers.
A <strong class="highlight">poster spotlight</strong> will give authors of contributed papers a <strong class="highlight">1-minute pitch</strong>, leading to a <strong class="highlight">poster session</strong>.
</p>
<p>
The workshop will feature a <strong class="highlight">round-table</strong> panel on
<span class="panel-title">Physics-Aware Learning: from Small Neural Networks to World Foundation Models.</span>
The workshop will be held in-person in <strong class="highlight">Vienna, Austria</strong>. Additionally, we welcome participants to join virtually via <strong class="highlight">Zoom</strong>.
</p>
<div class="table-wrapper">
<table>
<thead>
<tr>
<th>Time</th>
<th>Event</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>08:30 - 08:35</td>
<td>Opening</td>
<td>Welcome introduction (5 min)</td>
</tr>
<tr>
<td>08:35 - 08:55</td>
<td>Invited Talk 1</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>08:55 - 09:15</td>
<td>Invited Talk 2</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>09:15 - 09:35</td>
<td>Invited Talk 3</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>09:35 - 09:55</td>
<td>Poster Spotlight</td>
<td>20 min: ≈1 min per poster</td>
</tr>
<tr>
<td>09:55 - 10:25</td>
<td>Posters & Break</td>
<td>30 min: Poster session + coffee</td>
</tr>
<tr>
<td>10:25 - 10:45</td>
<td>Invited Talk 4</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>10:45 - 11:05</td>
<td>Invited Talk 5</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>11:05 - 11:25</td>
<td>Invited Talk 6</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>11:25 - 11:45</td>
<td>Invited Talk 7</td>
<td>15 min + 5 min Q&A</td>
</tr>
<tr>
<td>11:45 - 12:25</td>
<td>Round Table</td>
<td>40 min: Panel discussion</td>
</tr>
<tr>
<td>12:25 - 12:30</td>
<td>Closing</td>
<td>Best paper award & farewell</td>
</tr>
</tbody>
</table>
</div>
</section>
<!-- Call for Papers -->
<section id="cta" class="main">
<header class="major">
<h2>Call for Papers</h2>
</header>
<p>
This workshop aims to explore the synergies between data-driven and physics-based approaches in robotics. We invite submissions related to the following topics:
</p>
<ul>
<li><strong>Beyond black-box learning:</strong> How can we fuse physics-based knowledge with data-driven learning to enhance accuracy and generalization?</li>
<li><strong>Physics-informed, physics-encoded, physics-guided machine learning and neural operators:</strong> How can domain knowledge be injected into training loss functions, model architectures, constraints, or synthetic data generation pipelines?</li>
<li><strong>Physics-grounded foundation models</strong> for embodied intelligence: Can large-scale models be constrained or guided by physics to ensure safety, trustworthiness, and transferability to embodied systems?</li>
<li><strong>From correlation to causation:</strong> How can physics-driven machine learning approaches discover causal structures and enhance interpretability in decision-making?</li>
<li><strong>Applications and challenges across robotics domains:</strong> From modeling and state estimation to planning, control, and decision-making.</li>
</ul>
<p style="margin-bottom: 0.5em;">
Papers must be prepared according to the ICRA'26 format, and can have 2-8 pages. We also encourage submitting new ideas, even if not fully developed yet.
</p>
<p style="margin-bottom: 0.5em;">
Papers will be evaluated for quality, relevance to the workshop theme, clarity, and whether claims are well-supported by theory or experiments.
</p>
<p style="margin-bottom: 1em;">
All accepted contributions will be presented as posters during our poster session. Accepted papers will be posted on the workshop website, and will not be part of the ICRA conference proceedings.
</p>
<p>
A <strong>Best Paper Award</strong> will be given to the best contributed paper, selected by the program committee based on its quality and significance in the context of the workshop topics. The awardee will receive a certificate in recognition of their contribution.
</p>
</section>
<!-- Organizers -->
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<header class="major">
<h2>Organizers</h2>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4"><span class="image fit"><img src="images/organizers/Mattia_Piccinini.JPG" alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: contain;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/organizers/Johannes_Betz.png" alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/organizers/Gastone_Papini.jpg" alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
</div>
</div>
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<div class="col-4" , align="center">
<b><a href="https://www.mos.ed.tum.de/en/avs/team/mattia-piccinini/">Mattia Piccinini</a></b>
<h5>TUM Global Post-doc Researcher</h6>
<h5>Technical University of Munich</h5>
</div>
<div class="col-4" , align="center">
<b><a href="https://www.mos.ed.tum.de/en/avs/team/prof-dr-ing-johannes-betz/">Johannes Betz</a></b>
<h5>Assistant Professor</h5>
<h5>Technical University of Munich</h5>
</div>
<div class="col-4" , align="center">
<b><a href="https://tonegas.it/">Gastone Pietro Rosati Papini</a></b>
<h5>Associate Professor</h5>
<h5>University of Trento</h5>
</div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4"><span class="image fit"><img src="images/organizers/Baha_Zarrouki.jpeg" alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
<div class="col-4"><span class="image fit"><img src="images/organizers/Dingrui_Wang.png" alt="" style="width: 100%; height: auto; aspect-ratio: 1; object-fit: cover;" /></span></div>
</div>
</div>
<div class="box alt">
<div class="row gtr-50 gtr-uniform">
<div class="col-4" , align="center">
<b><a href="https://www.mos.ed.tum.de/en/avs/team/prof-dr-ing-johannes-betz-2/">Baha Zarrouki</a></b>
<h5>PhD Researcher</h5>
<h5>Technical University of Munich</h5>
</div>
<div class="col-4" , align="center">
<b><a href="https://www.mos.ed.tum.de/en/avs/team/dingrui-wang/">Dingrui Wang</a></b>
<h5>PhD Researcher</h5>
<h5>Technical University of Munich</h5>
</div>
</div>
</div>
<hr>
<center>
<p style="font-size: 0.8em;">
This workshop is supported by the IEEE RAS Technical Committee on
<em>Autonomous Ground Vehicles and Intelligent Transportation Systems</em>,
and by the Italian FIS 2 Call, Grant Assignment Decree No. 1236 adopted on
01/08/2023 by the Italian Ministry of University and Research (MUR), for the project
<q>“Structured neural network framework for modeling and control of autonomous systems – Neu4mes”</q>,
CUP E53C24003800001.
</p>
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