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content="Persistent Object Gaussian Splat for Tracking Human and Robot Manipulation of Irregularly Shaped Objects.">
<meta name="keywords" content="NeRF, GS, Gaussian Splatting, Feature Fields, Robotics">
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<title>POGS: Persistent Object Gaussian Splat for Tracking Human and Robot Manipulation of Irregularly Shaped Objects</title>
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<h1 class="title is-1 publication-title">POGS: Persistent Object Gaussian Splat for Tracking Human and Robot Manipulation of Irregularly Shaped Objects</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://uynitsuj.github.io">Justin Yu</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="">Kush Hari</a><sup>*1</sup>,</span>
<span class="author-block">
<a href="https://el-refai.github.io/">Karim El-Refai</a><sup>*1</sup>,
</span>
<span class="author-block">
<a href="">Arnav Dalal</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Justin Kerr</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Chung-Min Kim</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Richard Cheng</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="">Muhammad Zubair Irshad</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="">Ken Goldberg</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of California, Berkeley</span>
<span class="author-block"><sup>2</sup>Toyota Research Institute</span>
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<h2 class="subtitle has-text-centered">
<span class="dnerf"><b>POGS</b></span> persistently tracks object states in the real world robot workspace.
</h2>
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<h2 class="subtitle has-text-centered">
<span class="dnerf"><b>POGS</b></span> allows robots to react and adapt to changes in manipulated object states.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Tracking and manipulating irregularly-shaped, previously unseen objects in dynamic environments is important for robotic applications in manufacturing, assembly, and logistics. Recently introduced Gaussian Splats efficiently model object geometry, but lack persistent state estimation for taskoriented manipulation. We present <b>Persistent Object Gaussian Splat (POGS)</b>, a system that embeds semantics, self-supervised visual features, and object grouping features into a compact representation that can be continuously updated to estimate the pose of scanned objects. POGS updates object states without requiring expensive rescanning or prior CAD models of objects. After an initial multi-view scene capture and training phase, POGS uses a single stereo camera to integrate depth estimates along with self-supervised vision encoder features for object pose estimation. POGS supports grasping, reorientation, and natural language-driven manipulation by refining object pose estimates, facilitating sequential object reset operations with human-induced object perturbations and tool servoing, where robots recover tool pose despite tool perturbations of up to 30 degrees. POGS achieves up to 12 consecutive successful object resets and recovers from 80% of in-grasp tool perturbations
</p>
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<h2 class="title is-3">Results</h2>
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<style>
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display: flex;
flex-direction: column;
width: 100%;
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align-items: center;
box-sizing: border-box;
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<div class="content-container">
<!-- Heading -->
<h3 class="results-text">
<b>Click the thumbnails below to navigate between different interactive scenes from POGS state estimation results</b>
</h3>
<!-- Iframe Container - This will hold all your iframes -->
<div id="iframe-container" class="iframe-container">
<iframe id="drill_scene1" class="iframe show" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/drill_scene1.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="drill_scene2" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/drill_scene2.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="drill_scene3" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/drill_scene3.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="drill_scene4" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/drill_scene4.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="iron_shelf_pick1" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/iron_shelf_pick1.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="iron_shelf_place1" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/iron_shelf_place1.viser&initialCameraPosition=-0.12,-1.13,0.173&initialCameraLookAt=0.06,-0.5,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="iron_shelf_pick2" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/iron_shelf_pick2.viser&initialCameraPosition=-0.116,-1.146,0.173&initialCameraLookAt=0.063,-0.539,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
<iframe id="iron_shelf_place2" class="iframe" src="https://berkeleyautomation.github.io/POGS/viser-client/?playbackPath=https://berkeleyautomation.github.io/POGS/recordings/iron_shelf_place2.viser&initialCameraPosition=-0.12,-1.13,0.173&initialCameraLookAt=0.06,-0.5,-0.157&initialCameraUp=0.000,0.000,1.000"></iframe>
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Iron Shelf Pick #2
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Iron Shelf Place #2
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Shoe Shoebox Pick #1
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Shoe Shoebox Place #1
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Shoe Shoebox Pick #2
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Shoe Shoebox Place #2
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Jigsaw Shelf Pick #1
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Jigsaw Shelf Place #1
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Jigsaw Shelf Pick #2
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Jigsaw Shelf Place #2
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<!--/ Abstract. -->
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<h2 class="title is-3">Video</h2>
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<iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
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<!--/ Paper video. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{yu2025pogs,
author = {Yu, Justin and Hari, Kush and El-Refai, Karim and Dalil, Arnav and Kerr, Justin and Kim, Chung-Min and Cheng, Richard, and Irshad, Muhammad Z. and Goldberg, Ken},
title = {Persistent Object Gaussian Splat (POGS) for Tracking Human and Robot Manipulation of Irregularly Shaped Objects},
journal = {ICRA},
year = {2025},
}</code></pre>
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