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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Stem-Ob: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion">
<meta name="keywords" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Stem-Ob: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion
Inversion</title>
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</head>
<section class="hero">
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<div class="container is-fullhd">
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<div class="column has-text-centered">
<h1 class="title is-1 publication-title">
Stem-Ob: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion
Inversion
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a target="_blank" href="https://hukz18.github.io/">Kaizhe Hu</a><sup>123*</sup>,
</span>
<span class="author-block">
<a target="_blank" href="https://github.com/UFishs/">Zihang Rui</a><sup>1*</sup>,
</span>
<span class="author-block">
<a target="_blank" href="https://shockwavehe.github.io/">Yao He</a><sup>4</sup>,
</span>
<span class="author-block">
<a target="_blank" href="https://yuyaoliu.me/">Yuyao Liu</a><sup>1</sup>,
</span>
<span class="author-block">
<a target="_blank" href="https://piao-0429.github.io/">Pu Hua</a><sup>123</sup>,
</span>
<span class="author-block">
<a target="_blank" href="http://hxu.rocks/">Huazhe Xu</a><sup>123</sup>
</span>
</div>
<div class="logos">
<img src="./media/logos/tsinghua.png" alt="Tsinghua Logo" class="logo">
<img src="./media/logos/sqz.png" alt="Shanghai Qizhi Institute Logo" class="logo">
<img src="./media/logos/sail.png" alt="SAIL Logo" class="logo">
<img src="./media/logos/stanford.png" alt="Stanford Logo" class="logo">
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</div>
<div class="is-size-5 affiliation">
<sup>1</sup>Tsinghua University
<sup>2</sup>Shanghai Qizhi Institute
<sup>3</sup>Shanghai Artificial Intelligence Laboratory
<sup>4</sup>Stanford University
<sup></sup>
</div>
<br>
<div class="affiliation-note">
<sup>*</sup> Equal contribution
</div>
<div class="button-container">
<span class="link-block">
<a href="./stem-ob.pdf"
target="_blank" class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-pdf"></i>
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<span>PDF</span>
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<a href="https://github.com/hukz18/Stem-Ob-Code"
target="_blank" 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="https://arxiv.org/abs/2411.04919"
target="_blank" class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
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<span>ArXiv</span>
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</div>
</section>
<section class="hero teaser">
<div class="container is-max-widescreen">
<div class="hero-body">
<div class="container">
<div class="columns is-vcentered is-centered">
<img src="media/figures/teaser.png" class="teaser-image" />
</div>
</div>
</div>
</div>
<div class="container is-max-widescreen">
<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>
Visual imitation learning methods demonstrate strong performance, yet they lack generalization when faced
with visual input perturbations, including variations in lighting and textures, impeding their real-world
application. We propose Stem-Ob that utilizes pretrained image diffusion models to suppress low-level visual
differences while maintaining high-level scene structures. This image inversion process is akin to
transforming the observation into a shared representation, from which other observations stem, with
extraneous details removed. Stem-Ob contrasts with data-augmentation approaches as it is robust to various
unspecified appearance changes without the need for additional training. Our method is a simple yet highly
effective plug-and-play solution. Empirical results confirm the effectiveness of our approach in simulated
tasks and show an exceptionally significant improvement in real-world applications, with an average increase
of 22.2% in success rates compared to the best baseline.
</p>
</div>
</div>
</div>
<hr class="rounded">
<div class="rows">
<h2 class="title is-3">Overview of Stem-Ob</h2>
<div class="columns is-centered has-text-centered">
<iframe width="1120" height="630" src="https://www.youtube.com/embed/dgXJmaAETV0?si=TVpetTAVA554-ART"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</iframe>
</div>
<h2 class="title is-3">Pipeline of Stem-Ob</h2>
<img src="media/figures/pipeline.png" class="method-image" />
<p class="content has-text-justified">
<div class="formula">
<p><strong>(a)</strong> Our method has been evaluated in both real-world and simulated environments.
<strong>(b)</strong> The trained visual IL policies are directly applied to the original observation space \(
\mathcal{O} \), demonstrating robustness to unseen environmental disturbances. <strong>(c)</strong> We train
the visual IL policy \( \boldsymbol{\pi} \) on the diffusion-inversed latent space \(
\hat{\mathcal{O}}^{\hat{t}/T} \), where \( \hat{t} \) denotes a specific inversion step out of a total of \( T
\). Each composite rectangle in the diffusion inversion process, made up of three smaller sections, represents
the latent vector of an image, with finer attributes (gray) depicted as the smaller section. During the
inversion process, finer attributes converge earlier than coarser ones.
</p>
</div>
</p>
</div>
<hr class="rounded">
<div class="rows">
<h2 class="title is-3">Real-World Experiments</h2>
<hr class="rounded">
<h2 class="title is-4">Experiment Setup</h2>
<img src="media/figures/real-world_setup_overview.png" class="method-image" />
<p class="content has-text-justified">
<div class="formula">
<p><strong>(a)</strong> Overview of the whole setup. <strong>(b)(c)</strong> These tasks are performed by the
robot in a real-world environment, from left to right: <strong><em>Cup2Plate</em></strong>, <strong><em>Turn
on Faucet</em></strong>, <strong><em>Open Drawer</em></strong>, and <strong><em>Duck2Bowl</em></strong>.
The figure showcases the initial and final states of the tasks.</p>
</div>
</p>
<hr class="rounded">
<h2 class="title is-4">Experiment Results</h2>
<div class="columns is-centered has-text-centered">
<div class="column">
<img src="media/figures/real-world_results.png" class="method-image" />
</div>
</div>
<p class="content has-text-justified">
<div class="formula">
<p><strong>Evaluation of real-world experiments.</strong> Train.: evaluations in the same settings as the
training dataset. Gen.: evaluations under different visual perturbations for generalizability analysis. All:
evaluations including both Train. and Gen. The tasks are <strong>C2P</strong> (<strong><em>Cup to
Plate</em></strong>), <strong>D2B</strong> (<strong><em>Duck to Bowl</em></strong>), <strong>OD</strong>
(<strong><em>Open Drawer</em></strong>), and <strong>ToF</strong> (<strong><em>Turn on Faucet</em></strong>).
We report the mean and standard deviation of the success rate (%) over 6 settings for each task, and the best
results are highlighted in <strong>bold</strong>.</p>
</div>
</p>
</div>
<hr class="rounded">
<div class="rows">
<h2 class="title is-4">Visualization</h2>
<h3 class="title is-5">Cup to Plate</h3>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_trainW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_trainC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_cubeW.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_cubeC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_leafW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/cup_leafC.mp4" type="video/mp4">
</video>
</div>
</div>
<h3 class="title is-5">Duck to Bowl</h3>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_trainW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_trainC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_redW.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_redC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_blueW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/duck_blueC.mp4" type="video/mp4">
</video>
</div>
</div>
<h3 class="title is-5">Open Drawer</h3>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_trainW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_trainC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_goldW.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_goldC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_paintW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/drawer_paintC.mp4" type="video/mp4">
</video>
</div>
</div>
<h3 class="title is-5">Turn on Faucet</h3>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_trainW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_trainC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_testW.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="columns">
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_testC.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_paintW.mp4" type="video/mp4">
</video>
</div>
<div class="column has-text-centered">
<video id="dist1" controls autoplay loop muted width="100%">
<source src="media/videos/faucet_paintC.mp4" type="video/mp4">
</video>
</div>
</div>
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