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index.html
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<!DOCTYPE html>
<html>
<head>
<link rel = "icon" href = "assets/title_image.png" type = "image/x-icon">
<!-- TODO -->
<meta charset="utf-8">
<meta name="description"
content="SALON Website ">
<meta name="keywords" content="SALON, Autonomous, Adaptation, ATV">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta property="og:image" content="assets/Thumbnail.png" /> <!-- TODO -->
<title>SALON</title>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() {
dataLayer.push(arguments);
}
gtag('js', new Date());
gtag('config', 'G-PYVRSFMDRL');
</script>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
<link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="./static/css/bulma-slider.min.css">
<link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="./static/css/index.css">
<link rel="icon" href="./static/images/favicon.svg">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script defer src="./static/js/fontawesome.all.min.js"></script>
<script src="./static/js/bulma-carousel.min.js"></script>
<script src="./static/js/bulma-slider.min.js"></script>
<script src="./static/js/index.js"></script>
<style>
.line-height-adjust {
line-height: 1.1;
}
.button-shadow {
box-shadow: 4px 4px 8px rgba(0, 0, 0, 0.2);
}
.blockMobile {
display: none; /* Hide the component by default */
}
.image-overlay {
position: absolute;
right: 0;
top: 50%;
transform: translateX(30%) translateY(-50%);
}
@media only screen and (min-width: 768px) {
.blockMobile {
display: block; /* Show the component for screen width 768px and above (desktop) */
}
}
.showMobile {
display: none; /* Hide the component by default */
}
.bar-image {
max-width: 100px;
height: auto;
}
@media only screen and (max-width: 768px) {
.showMobile {
display: block; /* Show the component for screen width 768px and above (desktop) */
}
.image-overlay {
position: relative; /* Move the image below the text */
top: 0;
transform: none; /* Remove vertical centering */
margin-top: 20px; /* Space between text and image */
margin-right: 0; /* Remove right alignment */
}
.bar-image {
display: none;
}
}
#dynamic-content {
position: relative; /* Ensure the content sections are positioned relative to this container */
min-height: 200px; /* Optional: Set a minimum height to prevent collapsing before content is displayed */
}
#dynamic-content .content-section {
position: absolute;
top: 0;
left: 0;
width: 100%;
opacity: 0;
visibility: hidden;
transition: opacity 0.5s ease, visibility 0.5s ease;
}
#dynamic-content .content-section.active {
opacity: 1;
visibility: visible;
position: relative; /* Allow active content to influence the height of #dynamic-content */
}
</style>
</head>
<body>
<nav class="navbar" role="navigation" aria-label="main navigation">
<div class="navbar-brand">
<a role="button" class="navbar-burger" aria-label="menu" aria-expanded="false">
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
<span aria-hidden="true"></span>
</a>
</div>
<div class="navbar-menu">
<div class="navbar-start" style="flex-grow: 1; justify-content: center;">
<a class="navbar-item" href="https://theairlab.org"> <!-- TODO -->
<span class="icon">
<i class="fas fa-home"></i>
</span>
</a>
</div>
</div>
</nav>
<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-2 publication-title">SALON: Self-supervised Adaptive Learning for Off-road Navigation</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://matthewjsiv.github.io/">Matthew Sivaprakasam</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://striest.github.io/">Samuel Triest</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://cherieho.com/">Cherie Ho</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://shubhraaich.github.io/">Shubhra Aich</a><sup>1</sup>,</span>
<span class="author-block">
<a href="http://www.linkedin.com/in/jericlew">Jeric Lew</a><sup>2</sup>,</span>
<span class="author-block">
<a href="">Isaiah Adu</a><sup>3</sup>,</span>
<span class="author-block">
<a href="http://www.wangwenshan.com/">Wenshan Wang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://theairlab.org/">Sebastian Scherer</a><sup>1</sup>,</span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block"><sup>1</sup>Carnegie Mellon University</a>,
<sup>2</sup>National University of Singapore</a>,
<sup>3</sup>Pennsylvania State University</a>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="assets/Adaptive_Costmaps_ICRA_2025__arxiv_.pdf"
class="external-link button is-normal ">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2412.07826"
class="external-link button is-normal ">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Video Link. -->
<span class="link-block">
<a href="https://drive.google.com/file/d/1rMKKNeytyOUACF0fpVCuPbIQqTxWyWsj/view?usp=sharing"
class="external-link button is-normal ">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span>
<!-- Code Link. -->
<span class="link-block">
<a href="https://github.com/castacks/salon_ws"
class="external-link button is-normal ">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- Dataset Link. -->
<!-- <span class="link-block">
<a href="#"
class="external-link button is-normal" disabled="true">
<span class="icon">
<i class="far fa-images"></i>
</span>
<span>Data</span>
</a>
</div> -->
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline width="100%">
<source src="./assets/videos/Turnpike_Merged_1_small.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="coolname">staying on trail while avoiding uncertain objects such as tires</span>
</h2>
</div>
<div class="item item-chair-tp">
<video poster="" id="steve" autoplay controls muted loop playsinline width="100%">
<source src="./assets/videos/Turnpike_Merged_2_small.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="coolname">preference for smooth vegetation, without human labels</span>
</h2>
</div>
<div class="item item-chair-tp">
<video poster="" id="steve" autoplay controls muted loop playsinline width="100%">
<source src="./assets/videos/Wheelchair_Merged_1_small.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="coolname">quick adaptation to novel terrain (cobblestone)</span>
</h2>
</div>
<div class="item item-chair-tp">
<video poster="" id="steve" autoplay controls muted loop playsinline width="100%">
<source src="./assets/videos/Anymal_Merged_1_small.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
<span class="coolname">easy deployment on other robots</span>
</h2>
</div>
<!-- <div class="item item-chair-tp">
<video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/chair-tp.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-shiba">
<video poster="" id="shiba" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/shiba.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-fullbody">
<video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/fullbody.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-blueshirt">
<video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/blueshirt.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-mask">
<video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/mask.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-coffee">
<video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/coffee.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-toby">
<video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/toby2.mp4"
type="video/mp4">
</video>
</div> -->
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img source src="assets/title_v7.png" />
<h2 class="subtitle has-text-centered">
<span class="coolname">with one prior hand-label, our system running SALON learns from its own experience in the real-world to predict where and how fast to drive.</span>
</h2>
</div>
</div>
</section>
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Overview</h2>
<div class="content has-text-justified">
<p>
<!-- Autonomous robot navigation in off-road environments presents a number of challenges due to its lack of structure, making it difficult to handcraft robust heuristics for diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, current works often rely on significant prior data, for example minutes of human teleoperation data for each terrain type, which is difficult to scale with more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrains to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments. -->
Robot autonomy in off-road environments presents a number of challenges that make it difficult to handcraft navigation heuristics that are robust to diverse scenarios. While learned methods using hand labels or self-supervised data improve generalizability, they often require a tremendous amount of data and can be vulnerable to domain shifts. To improve generalization in novel environments, recent works have incorporated adaptation and self-supervision to develop autonomous systems that can learn from their own experiences online. However, they often rely on significant prior data, for example minutes of human teleoperation data, which is difficult to scale to more environments and robots. To address these limitations, we propose SALON, a perception-action framework for fast adaptation of traversability estimates with minimal human input. SALON rapidly learns online from experience while avoiding out of distribution terrain to produce adaptive and risk-aware cost and speed maps. Within seconds of collected experience, our results demonstrate comparable navigation performance over kilometer-scale courses in diverse off-road terrain as methods trained on 100-1000x more data. We additionally show promising results on significantly different robots in different environments.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<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">VFMs + Proprioception + Smart Buffer Management = Fast Adaptation</h2>
<div class="content has-text-justified">
<img source src="assets/flowchart.png" />
<p>
Using visual foundation models (such as DINOv2) as feature extractors is key to our approach. By grounding their generalizable features with proprioceptive feedback, robots can quickly adapt their understanding of the world through their own experiences without a human in the loop.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small is-light">
<div class="hero-body">
<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">Fast Adaptation Example</h2>
<div class="content has-text-justified">
<img source src="assets/results/adaptation_fig8.png" />
<p>
Example of SALON's fast adaptation: Within 10 seconds of experiencing grass for the first time, SALON is able to quickly differentiate key terrains, such as, ideal short grass, riskier vegetation and lethal trees. </p>
</div>
</div>
</div>
</div>
</section>
<!-- <section class="section">
<div class="container is-max-desktop showMobile">
<div class="columns is-centered has-text-centered">
<div class="column">
<a id="interactive_demo"></a>
<h2 class="title is-3">Explore the Dataset!</h2>
<div class="content has-text-justified">
<p>
To view the demo, please visit this website on a device with a larger screen. (If you are on a larger screen, increase the size of this window and then refresh).
</p>
</div>
</div>
</div>
</div> -->
<section class="hero is-small">
<div class="hero-body">
<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">Autonomy Results</h2>
<div class="buttons is-centered">
<button class="button is-primary line-height-adjust button-shadow" onclick="showContent(1,1)">Autonomy<br />Course</button>
<button class="button is-link button-shadow" onclick="showContent(2,1)">Show Costmaps</button>
<button class="button is-info button-shadow" onclick="showContent(3,1)">Show Speedmaps</button>
<button class="button is-success button-shadow" onclick="showContent(4,1)">Metrics</button>
</div>
<div id="dynamic-content">
<div class="content-section active" id="content-1-1">
<!-- <h2 class="title is-3">Autonomy Course</h2> -->
<div class="content has-text-justified">
<p>We run our autonomy experiments on a Yamaha Viking All-Terrain Vehicle, with two courses shown below. <b>Course 1</b> consists of waypoints spaced 50m apart, and <b>Course 2</b> consists of waypoints with varying spacing up to 200m. The VLAD clusters used for feature generation were computed from sample images collected from the "training data" zone. For each run, the system is initialized with no prior environment interaction data, and a single high-cost tree label from the training data area.</p>
<img src="assets/courses.png" alt="Experiment Overview" />
</div>
</div>
<div class="content-section" id="content-1-2">
<div class="content has-text-justified">
<p>SALON is able to not only avoid lethal vegetation but also distinguish fine-grained terrain properties. Rough gravel in the middle of the trail below is higher cost than the smoother areas around it.</p>
<img src="assets/results/turnpike_hill.png" alt="Cosmtaps" />
</div>
</div>
<div class="content-section" id="content-1-3">
<div class="content has-text-justified">
<p>Prediction of speedmaps allows the system to go faster where appropriate. As seen below, it is predicted that the robot can drive faster on trail than in grass.</p>
<img src="assets/results/turnpike_speedmap.png" alt="Speedmaps" />
</div>
</div>
<div class="content-section" id="content-1-4">
<div class="content has-text-justified">
<p>We show our method on 5 laps of course 1 against 4 other baselines. With no prior experience except for one hand-label, it quickly adapts and by the second lap already demonstrates comparable performance to Velociraptor (the prior state of the art).</p>
<img src="assets/results/metrics.png" alt="metrics" />
</div>
</div>
<!-- <div class="content-section" id="content-1-4">
<div class="content has-text-justified">
<p>Example of SALON's fast adaptation: Within 10 seconds of experiencing grass for the first time, SALON is able to quickly differentiate key terrains, such as, ideal short grass, riskier vegetation and lethal trees.</p>
<img src="assets/results/adaptation_fig8.png" alt="Adaptation" />
</div>
</div> -->
</div>
</div>
<!-- <div class="column is-narrow is-align-self-center"> -->
<div class="column is-narrow image-overlay">
<img src="assets/colorbars.png" alt="colorbars" class="bar-image">
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small is-light">
<div class="hero-body">
<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">Robot+Sensor Generalizability</h2>
<div class="buttons is-centered">
<button class="button is-primary line-height-adjust button-shadow" onclick="showContent(5,2)">Wheelchair - <br />Urban</button>
<button class="button is-link line-height-adjust button-shadow" onclick="showContent(6,2)">Quadruped - <br />Urban + Nature</button>
</div>
<div id="dynamic-content">
<div class="content-section active" id="content-2-5">
<div class="content has-text-justified">
<p>Evaluation on wheelchair in urban environment: After driving over rough cobblestone, the system quickly recognizes within 5 seconds that it is much rougher than the smooth sidewalk.</p>
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<p>With the same amount of data as Wild Visual Navigation, our method is able to correctly cost lethal objects like trees and walls without incorrectly costing short grass. Like WVN, we leverage only use visual features, and geometric information is used only to place them in the map.</p>
<img src="assets/results/wvn_compare_v2.png" alt="Adaptation" />
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@misc{sivaprakasam2024salonselfsupervisedadaptivelearning,
title={SALON: Self-supervised Adaptive Learning for Off-road Navigation},
author={Matthew Sivaprakasam and Samuel Triest and Cherie Ho and Shubhra Aich and Jeric Lew and Isaiah Adu and Wenshan Wang and Sebastian Scherer},
year={2024},
eprint={2412.07826},
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url={https://arxiv.org/abs/2412.07826},
}
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