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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="TODO: project description">
<meta name="keywords" content="TODO">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>ConBaT: Control Barrier Transformer for Safe Policy Learning</title>
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="./static/css/bulma.min.css">
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</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://realm.mit.edu">
<span class="icon">
<i class="fas fa-home"></i>
</span>
</a>
<div class="navbar-item has-dropdown is-hoverable">
<a class="navbar-link">
More Research
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REALM Website
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<!-- <div class="navbar-dropdown">
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TODO: link to any other pages you want
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<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
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<h1 class="title is-1 publication-title">ConBaT: Control Barrier Transformer for Safe Policy Learning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://mengyuest.github.io/">Yue Meng*</a>,
</span>
<span class="author-block">
<a href="https://www.saihv.com/">Sai Vemprala<sup>‡</sup></a>,
</span>
<span class="author-block">
<a href="http://rogeriobonatti.com/">Rogerio Bonatti<sup>†</sup></a>,
</span>
<span class="author-block">
<a href="https://chuchu.mit.edu/">Chuchu Fan*</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=4D1n8scAAAAJ&hl=en">Ashish Kapoor<sup>‡</sup></a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">*Massachusetts Institute of Technology</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>†</sup>Microsoft Research</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>‡</sup>Scaled Foundations</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2303.04212.pdf"
class="external-link button is-normal is-rounded is-dark">
<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/2303.04212"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Video Link. -->
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<a href="https://youtu.be/rqcv1TpoWXc"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Supplementary Video</span>
</a>
</span>
<!-- <span class="link-block">
<a href="https://youtu.be/TODO"
class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Presentation Video</span>
</a>
</span> -->
<!-- Code Link. -->
<!-- <span class="link-block">
<a href="https://github.com/mengyuest/stl_npc"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
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<!-- <span class="link-block">
<a href="static/appendix.pdf"
class="external-link button is-normal is-rounded is-dark">
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<span>Supplementary</span>
</a>
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</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./resources/conbat_teaser.png"
class="center-image"
alt="TODO: alt text"/>
<h2 class="subtitle has-text-centered">
<br/>
(Left) An agent trained to imitate expert demonstrations may focus on the result of the task without explicit notions of safety. (Right) Our method ConBaT learns a safety critic on top of the control policy and uses this control barrier critic to optimize the policy for safe actions actively.
</h2>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./resources/conbat_arch.png"
class="center-image"
alt="TODO: alt text"/>
<h2 class="subtitle has-text-centered">
<br/>
ConBaT architecture - a causal Transformer opearates on state and action tokens with a critic to tell safety.
</h2>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Paper video. -->
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<h2 class="title is-3">Video</h2>
<div class="publication-video">
<!-- <iframe src="https://www.youtube.com/embed/TODO?rel=0&showinfo=0"
frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/rqcv1TpoWXc?si=9iZZCp1owVT1ruL8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
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</div>
<!--/ Paper video. -->
<!-- Abstract. -->
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<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for autonomous systems is challenging because of safety requirements: besides executing correct actions, an autonomous agent must also avoid the high cost and potentially fatal critical mistakes. Traditionally, self-supervised training mainly focuses on imitating previously observed behaviors, and the training demonstrations carry no notion of which behaviors should be explicitly avoided. In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion. ConBaT is inspired by the concept of control barrier functions in control theory and uses a causal transformer that learns to predict safe robot actions autoregressively using a critic that requires minimal safety data labeling. During deployment, we employ a lightweight online optimization to find actions that ensure future states lie within the learned safe set. We apply our approach to different simulated control tasks and show that our method results in safer control policies compared to other classical and learning-based methods such as imitation learning, reinforcement learning, and model predictive control.
</p>
<!-- <p>
TODO: paragraph 2
</p> -->
</div>
</div>
</div>
<!--/ Abstract. -->
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./resources/conbat_results.png"
class="center-image"
alt="TODO: alt text"/>
<h2 class="subtitle has-text-centered">
<br/>
Experimental results. Compared to other baseline methods, our method ConBaT achieves the lowest collision rate.
</h2>
</div>
</div>
</div>
</section>
<section class="section">
<div class="columns is-centered">
<h2 class="title is-3">More content coming soon!</h2>
</div>
</section>
<section class="section">
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<div class="columns is-centered"> -->
<!-- TODO: Main point 1 -->
<!-- <div class="column">
<div class="content">
<h2 class="title is-3">Better results than RL</h2>
<img src="./static/images/env_demo.png"
class="center-image simulation-image"
alt="TODO: alt text"/>
</div>
</div> -->
<!--/ TODO: Main point 1 -->
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<h2 class="title is-3">Stable tracking control</h2>
<img src="./static/images/car_cmp_vis.png"
class="center-image simulation-image"
alt="TODO: alt text"/>
</div>
</div>
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<h2 class="title is-3">TODO: main point 3</h2>
<img src="./static/images/TODO.png"
class="center-image simulation-image"
alt="TODO: alt text"/>
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</div> -->
<!--/ TODO: Main point 3 -->
<!-- Concurrent Work. -->
<!-- <div class="columns is-centered">
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<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
This work is part of a broader research thread around <emph>TODO</emph>, which allow us to TODO. TODO: e.g. For a survey of the field of learned certificates, see <a rel="survey" href="https://arxiv.org/pdf/2202.11762.pdf">this paper</a>.
</p>
<p>
Other work on TODO from our lab include:
<ul>
<li><a href="TODO.mit.edu">example 1</a>.</li>
</ul>
</p>
</div>
</div>
</div> -->
<!--/ Concurrent Work. -->
<!-- <div class="columns is-centered">
<div class="column">
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<h2 class="title is-3"><a href="static/appendix.pdf">Supplementary</a></h2>
</div>
</div>
</div>
<div class="columns is-centered"> -->
<!-- </div> -->
</section>
<!-- <section class="section">
<div class="columns is-centered">
<a href="static/appendix.pdf">
<span>Supplementary</span>
</a>
</div>
</section> -->
<!-- <section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{TODO,
author={TODO, TODO and TODO, TODO},
journal={TODO},
title={TODO},
year={TODO},
volume={TODO},
number={TODO},
pages={TODO},
doi={TODO}}
}</code></pre>
</div>
</section> -->
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