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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="Documentation for the NavWareSet dataset">
<meta property="og:title" content="NavWareSet"/>
<meta property="og:description" content="Documentation for the NavWareSet dataset"/>
<meta property="og:url" content="https://anr-navware.github.io/navwareset/"/>
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<meta name="keywords" content="Social navigation human-robot interaction dataset socially compliant navigation non-compliant navigation">
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<title>NavWareSet</title>
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<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://capynetics.github.io/" target="_blank">Johnata Brayan</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/sihao-deng-349528b0/?originalSubdomain=fr" target="_blank">Sihao Deng</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/armando-alves-neto-0b355147/" target="_blank">Armando Alves Neto</a><sup>1</sup>,</span>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/iaroslav-okunevich-61b838205/?originalSubdomain=de" target="_blank">Iaroslav Okunevich</a><sup>2</sup>,</span>
</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/tom%C3%A1%C5%A1-krajn%C3%ADk-91a49a7/?originalSubdomain=cz" target="_blank">Tomas Krajnik</a><sup>3</sup>,</span>
</span>
<span class="author-block">
<a href="https://www-sop.inria.fr/teams/pulsar/personnel/Francois.Bremond/" target="_blank">Francois Bremond</a><sup>4</sup>,</span>
</span>
<span class="author-block">
<a href="https://yzrobot.github.io/" target="_blank">Zhi Yan</a><sup>2,5</sup></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<sup>1</sup>UFMG,
<sup>2</sup>UTBM,
<sup>3</sup>CTU,
<sup>4</sup>Inria,
<sup>5</sup>ENSTA - IP Paris
</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://github.com/anr-navware/NavWareSet-Tutorials" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-chalkboard-teacher"></i>
</span>
<span>Tutorials</span>
</a>
</span>
<!-- Supplementary PDF link -->
<span class="link-block">
<a href="https://search-data.ubfc.fr/FR-13002091000019-2025-05-22" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-download"></i>
</span>
<span>Downloads</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/anr-navware" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Repo</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/anr-navware/NavWareSet-SFM" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-code"></i>
</span>
<span>SFM Repo</span>
</a>
</span>
<!-- ArXiv abstract Link -->
<span class="link-block">
<a href="https://hal.science/view/index/docid/5231729" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-hal"></i>
</span>
<span>Paper</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="responsive-video">
<video poster="" id="tree" autoplay controls muted loop>
<source src="static/videos/hero_video.mp4" type="video/mp4">
</video>
</div>
<h2 class="subtitle has-text-centered" style="margin-top:1em;">
<strong>Top-left</strong>: Lidar point cloud of the robot's environment;<br>
<strong>Top-right</strong>: Robot's own sensory data;<br>
<strong>Bottom-left</strong>: Synced point cloud with pedestrian's positions and robot's pose;<br>
<strong>Bottom-right</strong>: Extracted pedestrian and robot trajectories with environment map.
</h2>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
NavWareSet is a novel dataset designed to advance socially aware robot navigation. It offers multi-modal recordings of socially compliant and non-compliant robot trajectories in realistic indoor scenarios. Using two different robots across seven diverse setups, NavWareSet captures rich human-robot interactions and navigation challenges. With lidar, RGB-D, video, odometry, and annotated human positions, it provides a valuable source for analyzing and training navigation algorithms that prioritize human comfort and safety.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Dataset overview -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Overview</h2>
<div class="content">
<ul>
<li>Wide range of social navigation scenarios with both individual and group interactions.</li>
<li>Over 192 minutes of interaction data and over 172 minutes of annotated trajectories.</li>
<li>Over 1000 individually annotated human tracks and over 600 robot tracks.</li>
<li>Sensory data from the perspective of 2 different robots (Toyota HSR and Clearpath Jackal).</li>
<li>Stationary Ground-Truth Recording Station (GRS) with video camera and 3D LiDAR scans.</li>
<li>Occupancy grid of the obstacles in the environment.</li>
<li>Recording of both social and non-social behavior of the robots.</li>
</ul>
</div>
</div>
</section>
<!-- End dataset overview -->
<!-- Experimental Setup -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Experimental Setup</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="content has-text-justified">
<!-- Setup section -->
<p class="is-size-4 has-text-centered"><strong>Environment</strong></p>
<div class="columns is-vcentered is-multiline">
<div class="column is-12-mobile is-6-tablet">
<!-- add image below-->
<img src="static/images/new_setup.png" alt="Experimental Setup" class="responsive-img">
</div>
<div class="column is-12-mobile is-6-tablet">
The dataset was recorded in a broad garage space measuring 3.9 m × 10.7 m with a
lateral opening making it possible to elicit tracks in a straight line or around corners.
The environment is equipped with a Ground-Truth Recording Station (GRS) that includes a video
recorder and a 3D LiDAR scanner. For each scene goal positions were placed around the map according to
different social navigation scenarios. The teleoperator (green) and GRS (orange) are on a raised platform. The robot (red) moves centrally, with participants (blue) spread around.
Fixed obstacles are shown in gray.
</div>
</div>
<div class="column is-12">
<!-- add image below-->
<img src="static/images/setup.png" alt="Experimental Setup" class="responsive-img">
</div>
<!-- end of Setup section -->
<!-- Setup section -->
<p class="is-size-4 has-text-centered"><br><strong>Ground-Truth Recording Station (GRS)</strong></p>
<div class="columns is-multiline">
<div class="column is-12-mobile is-6-tablet">
<!-- add image below-->
<img src="static/images/grs.jpg" alt="Experimental Setup" class="responsive-img">
</div>
<div class="column is-12-mobile is-6-tablet">
<div class="is-flex is-align-items-center" style="height:100%; min-height:300px;">
<div class="responsive-video" style="width:100%;" >
<iframe width="100%" height="100%" title="angel.stl" frameborder="0" allowfullscreen
mozallowfullscreen="true" webkitallowfullscreen="true"
allow="clipboard-write; autoplay; fullscreen; xr-spatial-tracking"
src="https://thangs.com/model/1362968/embed?utm_source=embed"></iframe>
</div>
</div>
</div>
</div>
<br>
<p class="has-text-justified has-text-centered">
The Ground-Truth Recording Station (GRS) is equipped with an
<a href="https://www.intelrealsense.com/depth-camera-d455/" target="_blank" style="color: #1a0dab;">Intel® RealSense™ Depth Camera D455</a>
and a
<a href="https://cdn.robosense.cn/20200723161715_42428.pdf" target="_blank" style="color: #1a0dab;">Robosense RS-LiDAR-16 3D LiDAR</a>.
Both sensors are mounted on a custom 3D-printed frame, which is positioned on a tripod for stable data collection.
You can interact with the frame in the 3D viewport above (click and drag to rotate) or access the 3D model
<a href="https://thangs.com/designer/johnatabrayan/3d-model/grs.stl-1362968" target="_blank" style="color: #1a0dab;">here</a>.
</p>
<p class="has-text-justified has-text-centered">
To minimize occlusions, the GRS was positioned at a height of 2.17 m above the ground.
To enhance the resolution of the LiDAR data, the GRS was tilted 15° downward toward the area of interest.
</p>
<!-- end of Setup section -->
<!-- Setup section -->
<p class="is-size-4 has-text-centered"><br><strong>Robots</strong></p>
<div class="columns is-vcentered is-multiline">
<div class="column is-12-mobile is-6-tablet">
<!-- add image below-->
<img src="static/images/robots.jpg" alt="Experimental Setup" class="responsive-img">
</div>
<div class="column is-12-mobile is-6-tablet">
The robots used in the NavWareSet dataset are the
<a href="https://clearpathrobotics.com/jackal-small-unmanned-ground-vehicle/" target="_blank" style="color: #1a0dab;">Clearpath Jackal</a> and the
<a href="https://www.toyota-europe.com/news/2018/toyota-expanding-robotics-research-in-europe" target="_blank" style="color: #1a0dab;">Toyota HSR</a>.
The Jackal is a small, rugged robot designed for outdoor and indoor environments, equipped with a 3D LiDAR sensor and an RGB-D camera.
The HSR is a versatile human-support robot capable of performing household tasks, featuring a manipulator arm, a mobile base, and various sensors for safe and intelligent interaction with people and objects.
In most scenarios both robots were teleoperated by a human operator, first in a socially aware manner, and then in a non-aware manner.
</div>
</div>
<!-- Setup section -->
<p class="is-size-4 has-text-centered"><br><strong>Experiment description</strong></p>
<div class="columns is-multiline">
<div class="column is-12">
<!-- add image below-->
<img src="static/images/Experiment_description2.png" alt="Experimental Setup" class="responsive-img">
</div>
</div>
<br>
<p class="has-text-justified has-text-centered">
The experiment was designed to capture common social navigation scenarios. These scenarios were proposed by Francis et al. in their paper, <a href="https://arxiv.org/pdf/2306.16740" target="_blank" style="color: #1a0dab;">Principles and Guidelines for Evaluating Social Robot Navigation Algorithms</a>. They serve not only to facilitate data collection but also to provide a standardized basis for evaluating the performance of social navigation algorithms. A detailed description of the scenarios used in NavWareSet is provided in the table below.
</p>
<details class="expandable">
<summary> Show Scenarios Table</summary>
<div class="content">
<table border="1" cellpadding="5" cellspacing="0">
<thead>
<tr>
<th>Scenario Name</th>
<th>Scenario Description</th>
<th>Geom. Layout</th>
<th>Scientific Purpose</th>
<th>Robot Task</th>
<th>Human Behavior</th>
<th>Ideal Outcome</th>
</tr>
</thead>
<tbody>
<tr>
<td>FRONTAL APPROACH</td>
<td>A pedestrian and robot approach head-on.</td>
<td>Passable Space</td>
<td>Pedestrian Interaction</td>
<td>Navigate A to B</td>
<td>Navigate B to A</td>
<td>Robot / humans pass</td>
</tr>
<tr>
<td>PEDESTRIAN OBSTRUCTION</td>
<td>A pedestrian blocks the robot's path.</td>
<td>Passable Space</td>
<td>Pedestrian Interaction</td>
<td>Navigate A to B</td>
<td>Block path</td>
<td>Robot stops or reroutes</td>
</tr>
<tr>
<td>BLIND CORNER</td>
<td>A robot and human meet at a blind corner.</td>
<td>Corner</td>
<td>Pedestrian Interaction</td>
<td>Navigate A to B</td>
<td>Navigate B to A</td>
<td>No collision / obstruction</td>
</tr>
<tr>
<td>PERPENDICULAR TRAFFIC</td>
<td>Person moves perpendicular to robot.</td>
<td>Intersection</td>
<td>Pedestrian Interaction</td>
<td>Navigate A to B</td>
<td>Navigate B to A</td>
<td>No collision / obstruction</td>
</tr>
<tr>
<td>FOLLOWING HUMAN</td>
<td>A robot follows a person.</td>
<td>Walking Space</td>
<td>Joint Navigation</td>
<td>Follow human</td>
<td>Lead robot</td>
<td>Robot follows person</td>
</tr>
<tr>
<td>CIRCULAR CROSSING</td>
<td>People and robot move in paths that intersect in the center of a circle</td>
<td>Passable Space</td>
<td>Crowd Navigation</td>
<td>Navigate A to B</td>
<td>Navigate B to A</td>
<td>No collision / smooth flow</td>
</tr>
<tr>
<td>OBJECT HANDOVER</td>
<td>A robot hands an object to a human.</td>
<td>Passable Space</td>
<td>Interactive Navigation</td>
<td>Deliver/Receive object</td>
<td>Deliver/Receive object</td>
<td>Human/Robot takes object</td>
</tr>
</tbody>
</table>
</div>
</details>
<br>
<p class="has-text-justified has-text-centered">
In total 17 participants were assigned numbers and divided in 2 groups of 5 and 5 pairs.
Each group performed all scenarios but the object handover scenario. All scenarios were recorded for 4 minutes.
Most scenarios were performed with both robots, except for the object handover scenario which was only performed with the Toyota HSR.
Most scenarios were recorded twice, once with the robot navigating in a socially compliant manner and once in a non-compliant manner.
Social behavior adhered to predefined rules (maintaining social distances, avoiding abrupt movements),
while non-social behavior involved direct waypoint-to-waypoint navigation, disregarding human presence.
A detailed description of all recorded scenes used in NavWareSet is provided in the table below.
</p>
<details class="expandable">
<summary> Show Scenes Table</summary>
<div class="content">
<table border="1">
<thead>
<tr>
<th>N°</th>
<th>Name</th>
<th>Scenario</th>
<th>Robot</th>
<th>Robot Behavior</th>
<th>Group</th>
<th>Participants</th>
</tr>
</thead>
<tbody>
<tr><td>1</td><td>Scene 1</td><td>Frontal Approach</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>2</td><td>Scene 2</td><td>Pedestrian Obstruction</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>3</td><td>Scene 3</td><td>Blind Corner</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>4</td><td>Scene 4</td><td>Following Human</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>5</td><td>Scene 5</td><td>Perpendicular Traffic</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>6</td><td>Scene 6</td><td>Circular Crossing</td><td>HSR</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>7</td><td>Scene 8</td><td>Frontal Approach</td><td>HSR</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>8</td><td>Scene 9</td><td>Pedestrian Obstruction</td><td>HSR</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>9</td><td>Scene 10</td><td>Blind Corner</td><td>HSR</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>10</td><td>Scene 12</td><td>Perpendicular Traffic</td><td>HSR</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>11</td><td>Scene 13</td><td>Circular Crossing</td><td>HSR</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 4, 5</td></tr>
<tr><td>12</td><td>Scene 15</td><td>Frontal Approach</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>13</td><td>Scene 16</td><td>Pedestrian Obstruction</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>14</td><td>Scene 17</td><td>Blind Corner</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>15</td><td>Scene 18</td><td>Following Human</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>16</td><td>Scene 19</td><td>Perpendicular Traffic</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>17</td><td>Scene 20</td><td>Circular Crossing</td><td>Jackal</td><td>Social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>18</td><td>Scene 21</td><td>Frontal Approach</td><td>Jackal</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>19</td><td>Scene 22</td><td>Pedestrian Obstruction</td><td>Jackal</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>20</td><td>Scene 23</td><td>Blind Corner</td><td>Jackal</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>21</td><td>Scene 25</td><td>Perpendicular Traffic</td><td>Jackal</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>22</td><td>Scene 26</td><td>Circular Crossing</td><td>Jackal</td><td>Non-social</td><td>Group 1</td><td>1, 2, 3, 5, 6</td></tr>
<tr><td>23</td><td>Scene 27</td><td>Frontal Approach</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>24</td><td>Scene 28</td><td>Pedestrian Obstruction</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>25</td><td>Scene 29</td><td>Blind Corner</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>26</td><td>Scene 30</td><td>Following Human</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>27</td><td>Scene 31</td><td>Perpendicular Traffic</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>28</td><td>Scene 32</td><td>Circular Crossing</td><td>HSR</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>29</td><td>Scene 34</td><td>Frontal Approach</td><td>HSR</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>30</td><td>Scene 35</td><td>Pedestrian Obstruction</td><td>HSR</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>31</td><td>Scene 36</td><td>Blind Corner</td><td>HSR</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>32</td><td>Scene 38</td><td>Perpendicular Traffic</td><td>HSR</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>33</td><td>Scene 39</td><td>Circular Crossing</td><td>HSR</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>34</td><td>Scene 41</td><td>Frontal Approach</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>35</td><td>Scene 42</td><td>Pedestrian Obstruction</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>36</td><td>Scene 43</td><td>Blind Corner</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>37</td><td>Scene 44</td><td>Following Human</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>38</td><td>Scene 45</td><td>Perpendicular Traffic</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>39</td><td>Scene 46</td><td>Circular Crossing</td><td>Jackal</td><td>Social</td><td>Group 2</td><td>7, 8, 9, 10, 12</td></tr>
<tr><td>40</td><td>Scene 47</td><td>Frontal Approach</td><td>Jackal</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>41</td><td>Scene 49</td><td>Blind Corner</td><td>Jackal</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>42</td><td>Scene 51</td><td>Perpendicular Traffic</td><td>Jackal</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 11</td></tr>
<tr><td>43</td><td>Scene 52</td><td>Circular Crossing</td><td>Jackal</td><td>Non-social</td><td>Group 2</td><td>7, 8, 9, 10, 12</td></tr>
<tr><td>44</td><td>Scene 53</td><td>Object handover</td><td>HSR</td><td>Social</td><td>Pair 1</td><td>2, 8</td></tr>
<tr><td>45</td><td>Scene 54</td><td>Object handover</td><td>HSR</td><td>Social</td><td>Pair 2</td><td>1, 9</td></tr>
<tr><td>46</td><td>Scene 55</td><td>Object handover</td><td>HSR</td><td>Social</td><td>Pair 3</td><td>13, 14</td></tr>
<tr><td>47</td><td>Scene 56</td><td>Object handover</td><td>HSR</td><td>Social</td><td>Pair 4</td><td>4, 15</td></tr>
<tr><td>48</td><td>Scene 57</td><td>Object handover</td><td>HSR</td><td>Social</td><td>Pair 5</td><td>16, 17</td></tr>
</tbody>
</table>
</div>
</details>
<!-- end of Setup section -->
</div>
</div>
</div>
</div>
</div>
</section>
<!-- End experimental setup -->
<!-- Data Pipeline -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Data Pipeline</h2>
<div class="columns is-vcentered is-multiline">
<div class="column is-12-mobile is-6-tablet">
<!-- add image below-->
<img src="static/images/pipeline.png" alt="Experimental Setup" class="responsive-img">
</div>
<div class="column is-12-mobile is-6-tablet">
<ol style="text-align: justify;">
<li>
<strong>Experiment Design:</strong>
The pipeline begins with careful planning of social navigation scenarios in a controlled indoor environment.
At this step it is defined how robots and human participants will interact in both socially compliant and non-compliant modes.
This phase ensures that a diverse set of realistic interactions is captured.
</li>
<br>
<li>
<strong>Data Collection:</strong>
The robots (HSR and Jackal) and a Ground-Truth Recording Station (GRS) simultaneously collect raw sensory data.
The robots log LiDAR, RGB-D, stereo vision, IMU, odometry, and velocity commands.
Meanwhile, the stationary GRS records the entire scene from an external vantage point,
providing an independent reference for later annotation and validation.
</li>
<br>
<li>
<strong>Raw Data Organization:</strong>
All recorded sensor data is saved in ROS bag files.
Each scene’s raw data is packaged into separate files for the robot and the GRS.
This organized structure allows researchers to easily replay the scenarios and verify the consistency of recorded sensor streams.
</li>
<br>
<li>
<strong>Data Processing:</strong>
The raw data undergoes processing.
Robot poses are extracted using SLAM.
Human participant positions are manually annotated frame-by-frame using the CVAT tool applied to the GRS point cloud.
This ensures accurate human trajectory labeling, crucial for studying robot social navigation behavior.
All CVAT annotations were saved using the Supervisely format.
</li>
<br>
<li>
<strong>Data Product Generation:</strong>
After processing, clean and usable data products are created for each scene:
<ul style="margin-top: 0.5em; margin-bottom: 0.5em; padding-left: 1.5em; list-style-type: disc;">
<li>Annotated point clouds synchronized with robot poses.</li>
<li>Robot and participant positions in easily accessible CSV files.</li>
<li>Occupancy grids detailing static obstacles.</li>
<li>All metadata and calibration offsets are packaged to ensure reproducibility.</li>
</ul>
These structured files make the dataset valuable for training, benchmarking, and testing navigation algorithms.
</li>
<br>
<li>
<strong>Distribution:</strong>
The complete, organized dataset — including raw bags, annotations, CSVs, and occupancy grids — is shared publicly.
A series of tutorials and helper scripts are provided to assist researchers in using the dataset effectively.
The dataset is designed to be user-friendly, with clear documentation on how to access and utilize
the data for various research purposes.
Researchers can access everything through the
<a href="https://anr-navware.github.io/navwareset/" target="_blank">NavWareSet website</a> and the
<a href="https://github.com/anr-navware" target="_blank">NavWare GitHub repository</a>.
All data can be downloaded in bulk or individually using the
<a href="https://search-data.ubfc.fr/FR-13002091000019-2025-05-22" target="_blank">dat@UBFC</a> website.
</li>
</ol>
<!-- <h3>✅ Summary</h3>
<p>
This pipeline ensures that NavWareSet is not just raw data, but a high-quality, well-documented resource
designed to push forward research in robot social compliance and navigation in human environments.
</p> -->
</div>
</div>
</div>
</section>
<!-- End Data Pipeline -->
<!-- Data Overview -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<!-- Paper video. -->
<h2 class="title is-3">Data Overview</h2>
<p>
Each scene is divided into four files, each containing its own data.
All files start with the number of its respective scene name.
</p>
<p>
For generic scene "x" we have the following files:
</p>
<!-- add Expandable section below -->
<details class="expandable">
<summary> x_robot.zip</summary>
<div class="content">
<p>The zipped ROS bag with the raw data from the robot.</p>
<p>The available ROS topics for scenes with the <strong>HSR</strong> are:</p>
<ul>
<li>/hsrb/head_rgbd_sensor/rgb/camera_info</li>
<li>/hsrb/head_rgbd_sensor/rgb/image_rect_color</li>
<li>/hsrb/command_velocity</li>
<li>/hsrb/head_l_stereo_camera/image_raw</li>
<li>/hsrb/head_rgbd_sensor/depth_registered/camera_info</li>
<li>/static_obstacle_ros_map</li>
<li>/hsrb/head_l_stereo_camera/camera_info</li>
<li>/hsrb/head_r_stereo_camera/camera_info</li>
<li>/hsrb/head_rgbd_sensor/depth_registered/rectified_points</li>
<li>/tf</li>
<li>/hsrb/head_r_stereo_camera/image_raw</li>
<li>/hsrb/base_scan</li>
<li>/hsrb/head_rgbd_sensor/depth_registered/image_rect_raw</li>
<li>/tf_static</li>
<li>/dynamic_obstacle_map</li>
</ul>
<p>The available ROS topics for scenes with the <strong>Jackal</strong> are:</p>
<ul>
<li>/amcl_pose</li>
<li>/camera/color/image_raw</li>
<li>/map</li>
<li>/tf</li>
<li>/rslidar_points</li>
<li>/camera/color/camera_info</li>
<li>/cmd_vel</li>
</ul>
</div>
</details>
<!-- end of Expandable section -->
<!-- add Expandable section below -->
<details class="expandable">
<summary> x_grs.zip</summary>
<div class="content">
<p>The zipped ROS bag with the raw data from the Ground-Truth Recording Station (GRS).</p>
<p>The available ROS topics are:</p>
<ul>
<li>/camera/color/image_raw</li>
<li>/rslidar_points</li>
<li>/camera/color/camera_info</li>
</ul>
</div>
</details>
<!-- end of Expandable section -->
<!-- add Expandable section below -->
<details class="expandable">
<summary> x_annotated.zip</summary>
<div class="content">
<p>The zipped folder containing annotated filtered point cloud, synchronized with the robot’s poses for scene x,
organized following the structure below:</p>
<code>
x_annotated/<br>
├── scene_x/<br>
│ ├── pointcloud/<br>
│ │ ├── 1717330243.123456789.pcd<br>
│ │ ├── 1717330244.123456789.pcd<br>
│ │ └── ...<br>
│ ├── ann/<br>
│ │ ├── 1717330243.123456789.json<br>
│ │ ├── 1717330244.123456789.json<br>
│ │ └── ...<br>
├── x_robot_pose.csv<br>
├── x_grs_to_bot_offset.json<br>
├── x_occupancy_xy_points.json<br>
├── meta.json<br>
└── key_id_map.json<br>
</code>
</div>
</details>
<!-- end of Expandable section -->
<!-- add Expandable section below -->
<details class="expandable ">
<summary> x_poses.zip</summary>
<div class="content">
<p>The zipped folder containing participant and robot positions with the occupancy grid of the obstacles in scene x,
organized following the structure below:</p>
<code>
x_poses/<br>
├── x_occupancy_xy_points.json<br>
└── x_robot_and_participants.csv<br>
</code>
<br>
<p>The CSV file with the robot poses and the position of the participants follows the pattern below:</p>
<table border="1" cellpadding="5" cellspacing="0">
<thead>
<tr>
<th>timestamp</th>
<th>robot_x</th>
<th>robot_y</th>
<th>robot_yaw_rad</th>
<th>x1</th>
<th>y1</th>
<th>x2</th>
<th>y2</th>
<th>x3</th>
<th>y3</th>
<th>x4</th>
<th>y4</th>
<th>x5</th>
<th>y5</th>
</tr>
</thead>
<tbody>
<tr>
<td>1730220611292458240</td>
<td>2.7051</td>
<td>-0.8938</td>
<td>-0.0046</td>
<td>1.4400</td>
<td>0.5500</td>
<td>0.6800</td>
<td>-1.1600</td>
<td>10.6500</td>
<td>0.1800</td>
<td>11.0900</td>
<td>-1.5600</td>
<td>10.9200</td>
<td>-2.6500</td>
</tr>
<tr>
<td>1730220611392471296</td>
<td>2.7051</td>
<td>-0.8938</td>
<td>-0.0046</td>
<td>1.4300</td>
<td>0.5500</td>
<td>0.6800</td>
<td>-1.1600</td>
<td>10.6500</td>
<td>0.1800</td>
<td>11.0900</td>
<td>-1.5600</td>
<td>10.9200</td>
<td>-2.6500</td>
</tr>
<tr>
<td>1730220611492328448</td>
<td>2.7051</td>
<td>-0.8938</td>
<td>-0.0046</td>
<td>1.4300</td>
<td>0.5500</td>
<td>0.6800</td>
<td>-1.1600</td>
<td>10.6500</td>
<td>0.1800</td>
<td>11.0900</td>
<td>-1.5600</td>
<td>10.9200</td>
<td>-2.6500</td>
</tr>
</tbody>
</table>
</div>
</details>
<!-- end of Expandable section -->
<br>
<p>
Scenes 53 to 57 don't have person position annotation because the participants don't walk around the environment;
only the robot moves between the participants.
</p>
</div>
</div>
</section>
<!-- End Data Overview -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title is-3">BibTeX</h2>
<pre><code>@unpublished{brayan:hal-05231729,
TITLE = {{NavWareSet: A Dataset of Socially Compliant and Non-Compliant Robot Navigation}},
AUTHOR = {Brayan, Johnata and Deng, Sihao and Alves Neto, Armando and Okunevich, Iaroslav and Krajnik, Tomas and Bremond, Francois and Yan, Zhi},
URL = {https://hal.science/hal-05231729},
NOTE = {working paper or preprint},
YEAR = {2025},
MONTH = Aug,
DOI = {10.1177/ToBeAssigned},
KEYWORDS = {Social navigation ; Human-robot interaction ; Dataset ; Socially compliant navigation ; Non-compliant navigation},
PDF = {https://hal.science/hal-05231729v1/file/NavWareSet.pdf},
HAL_ID = {hal-05231729},
HAL_VERSION = {v1},
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
<!-- Authors -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Any Doubts?</h2>
<p>
Feel free to open an issue on our GitHub repository, we will happily assist you!
</p>
<div class="has-text-centered" style="margin-top: 2em;">
<a class="button is-link is-large" href="https://github.com/anr-navware/NavWareSet/issues/new" target="_blank">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Open an Issue</span>
</a>
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</div>
</section>
<!-- End of Authors section-->
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<div class="hero-body">
<div class="container">
<h2 class="title is-3">Funding</h2>
<div class="columns is-vcentered is-multiline">
<div class="column is-12-mobile is-4-tablet has-text-centered">
<img src="static/images/Logo-ANR-300x300.png" alt="Experimental Setup" style="width: 60%; height: auto; border-radius: 8px;">
</div>
<div class="column is-12-mobile is-8-tablet">
This project has received funding from the French National Research Agency (ANR) under grant agreement No ANR-23-CE10-0016. It is also supported by the Toyota Partner Robot joint research project and was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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</section>
<!-- End of funding section-->
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