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<!DOCTYPE html>
<html>
<head>
<title>DecisionNCE</title>
<style>
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content="Embodied Multimodal Representations via Implicit Preference Learning">
<meta name="keywords" content="DecisionNCE, Representation Learning, Embodied AI, AI">
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<title>Embodied Multimodal Representations via Implicit Preference Learning</title>
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More Research
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<b>OMIGA</b> <p style="font-size:18px; display: inline; margin-left: 5px;"></p>
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<h1 class="title is-1 publication-title is-bold">
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<span class="mmmu" style="vertical-align: middle">DecisionNCE</span>
</h1>
<h2 class="subtitle is-3 publication-subtitle">
Embodied Multimodal Representations via Implicit Preference Learning
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and Reasoning Benchmark for Expert AGI -->
</h2>
<div class="is-size-5 publication-authors">
<span class="author-block">Jianxiong Li* <sup>†</sup> <sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Jinliang Zheng* <sup style="color:#DC4437;">1</sup> <sup style="color:#4385F5;">2</sup>,</span>
<span class="author-block">Yinan Zheng* <sup style="color:#DC4437;">1</sup>,</span><br>
<span class="author-block">Liyuan Mao<sup style="color:#109D59;">3</sup>,</span>
<span class="author-block">Xiao Hu<sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Sijie Cheng<sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Haoyi Niu<sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Jihao Liu<sup style="color:#F5B400;">4</sup> <sup style="color:#4385F5;">2</sup>,</span><br>
<span class="author-block">Yu Liu<sup style="color:#4385F5;">2</sup>,</span>
<span class="author-block">Jingjing Liu<sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Ya-Qin Zhang<sup style="color:#DC4437;">1</sup>,</span>
<span class="author-block">Xianyuan Zhan<sup>✉</sup> <sup style="color:#DC4437;">1</sup> <sup style="color:#f542dd;">5</sup>,</span>
</div>
<br>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup style="color:#DC4437;">1</sup>AIR, Tsinghua University</span>
<span class="author-block"><sup style="color:#4385F5;">2</sup>SenseTime Research</span>
<span class="author-block"><sup style="color:#109D59;">3</sup>Shanghai Jiaotong University</span><br>
<span class="author-block"><sup style="color:#F5B400;">4</sup>CUHK MMLab</span>
<span class="author-block"><sup style="color:#f542dd;">5</sup>Shanghai AI Lab</span>
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<br>
<div class="is-size-5 publication-authors">
<span class="author-block">*Equal contribution, </span><br>
<span class="author-block">†Project Lead:</span>
<span class="author-block"><a href="mailto:li-jx21@mails.tsinghua.edu.cn">li-jx21@mails.tsinghua.edu.cn</a></span><br>
<span class="author-block">✉Corresponding author:</span>
<span class="author-block"><a href="mailto:zhanxianyuan@air.tsinghua.edu.cn">zhanxianyuan@air.tsinghua.edu.cn</a></span>
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<section class="hero teaser">
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<div class="hero-body">
<video id="matting-video" autoplay muted controls playsinline width="100%" >
<source src="./assets/web/teaser.mp4" type="video/mp4">
</video>
</div>
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</section>
<section class="section">
<div class="container" style="margin-bottom: 2vh;">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h1 class="title is-2">Abstraction</h1>
<div class="content has-text-justified">
<p>
Multimodal pretraining has emerged as an effective strategy for
the trinity of goals of representation learning in autonomous robots:
1) extracting both local and global task progression information; 2) enforcing temporal consistency of visual representation; 3) capturing trajectory-level language grounding.
Most existing methods approach these via separate objectives, which often reach sub-optimal solutions.
In this paper, we propose a universal unified objective that can simultaneously extract meaningful task progression information from image sequences and seamlessly align them with language instructions.
We discover that via implicit preferences, where a visual trajectory inherently aligns better with its corresponding language instruction than mismatched pairs, the popular Bradley-Terry model can transform into representation learning through proper reward reparameterizations. The resulted framework, <b><i>DecisionNCE</i></b>, mirrors an InfoNCE-style objective but is distinctively tailored for decision-making tasks, providing an embodied representation learning framework that elegantly <b>extracts both local and global task progression features</b>, with temporal consistency enforced through implicit time contrastive learning, while <b>ensuring trajectory-level instruction grounding</b> via multimodal joint encoding.
Evaluation on both simulated and real robots demonstrates that DecisionNCE effectively facilitates diverse downstream policy learning tasks, offering a versatile solution for unified representation and reward learning.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container">
<div class="columns is-centered has-text-centered">
<!-- <div class="column is-full-width has-text-centered"> -->
<div class="column is-four-fifths">
<h1 class="title is-2">Downstream Control Tasks Results</h1>
<div class="content has-text-justified">
<p>
The DecisionNCE encoders are pretrained using large-scale human video dataset <a href="https://epic-kitchens.github.io/2023">EpicKitchen</a>.
We freeze the pretrained vision-language encoders and use their output representations as input to a 256-256 MLP to train LCBC policies.
</p>
<div class="column">
<h2 style="text-align:left" class="title is-3">Results on Real Robots</h2>
<div class="content has-text-justified">
<div class="content has-text-centered">
<img src="./assets/web/realrobot.jpg" alt="algebraic reasoning" class="center">
<p><b><i>Figure 1:</i></b> Real robot LCBC experimental results. Success rate is averaged over 10 episodes and 3 seeds.</p>
</div>
<div class="column">
<h3 style="text-align:left" class="title is-4">Red cup on silver pan</h3>
<div class="content has-text-justified">
</div>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupsilverpan/1.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupsilverpan/2.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupsilverpan/3.mp4"
type="video/mp4">
</video>
</div>
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<h3 style="text-align:left" class="title is-4">Red cup on red plate</h3>
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupredplate/1.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupredplate/2.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/redcupredplate/3.mp4"
type="video/mp4">
</video>
</div>
<div class="column">
<h3 style="text-align:left" class="title is-4">Duck on green plate</h3>
<div class="content has-text-justified">
</div>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckgreenplate/1.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckgreenplate/2.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckgreenplate/3.mp4"
type="video/mp4">
</video>
</div>
<div class="column">
<h3 style="text-align:left" class="title is-4">Duck in pot</h3>
<div class="content has-text-justified">
</div>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckpot/1.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckpot/2.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/duckpot/3.mp4"
type="video/mp4">
</video>
</div>
<div class="column">
<h3 style="text-align:left" class="title is-4">Move pot</h3>
<div class="content has-text-justified">
</div>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/movepot/1.mp4"
type="video/mp4">
</video>
<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/movepot/2.mp4"
type="video/mp4">
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<source src="./assets/web/movepot/3.mp4"
type="video/mp4">
</video>
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<div class="column">
<h3 style="text-align:left" class="title is-4">Fold cloth</h3>
<div class="content has-text-justified">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/foldcloth/1.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/foldcloth/2.mp4"
type="video/mp4">
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<source src="./assets/web/foldcloth/3.mp4"
type="video/mp4">
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<h3 style="text-align:left" class="title is-4">Flip the red cup upright</h3>
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<source src="./assets/web/flip/1.mp4"
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<source src="./assets/web/flip/2.mp4"
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<h3 style="text-align:left" class="title is-4">Open the microwave</h3>
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/openmicrowave/1.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/openmicrowave/2.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/openmicrowave/3.mp4"
type="video/mp4">
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<h3 style="text-align:left" class="title is-4">Close the microwave</h3>
<div class="content has-text-justified">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/closemicrowave/1.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/closemicrowave/2.mp4"
type="video/mp4">
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<video id="dollyzoom" autoplay controls muted loop playsinline width="32.5%" height="100%">
<source src="./assets/web/closemicrowave/3.mp4"
type="video/mp4">
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<div class="column">
<h2 style="text-align:left" class="title is-3">Results on Simulation</h2>
<div class="content has-text-justified">
<p>
We also evaluate on the FrankaKitchen benchmark.
We train LCBC policies on 5 tasks in FrankaKitchen environment using 1/3/5 demonstrations for each task.
DecisionNCE achieves the highest success rate across diverse dataset quantities,
demonstrating its effectiveness in extracting valuable information from out-of-domain data.
</p>
<div class="content has-text-centered">
<img src="./assets/web/simulation.png" alt="algebraic reasoning" class="center">
<p><b><i>Figure 2:</i></b> Simulation LCBC results. Max success rate averaged over 25 evaluation episodes and 3 seeds.</p>
</div>
<img src="./assets/web/simulation/1.gif" alt="Image" style="width: 19%;">
<img src="./assets/web/simulation/2.gif" alt="Image" style="width: 19%;">
<img src="./assets/web/simulation/3.gif" alt="Image" style="width: 19%;">
<img src="./assets/web/simulation/4.gif" alt="Image" style="width: 19%;">
<img src="./assets/web/simulation/5.gif" alt="Image" style="width: 19%;">
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</div>
</section>
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<div class="container">
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<h2 class="title is-3">Feasibility-Guided Diffusion Model</h2>
<div class="content has-text-justified">
<p>
We propose a feasibility-dependent objective, i.e.,
<b>maximizing reward value within the feasible region while minimizing safety risks in the infeasible region</b>.
In FISOR, the optimal policy for the optimization problem can be derived in a special form of weighted behavior cloning.
Moreover, we propose a novel energy-guided sampling method that <b>does not require training a complicated time-dependent classifier</b> to simplify the training. <b>No more Lagrangian.</b>
</p>
<div class="content has-text-centered">
<img src="./assets/framework.jpg" alt="algebraic reasoning" class="center">
<p><b><i>Figure 2:</i></b> Feasibility-guided diffusion model with time-independent classifier-guided sampling method.</p>
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</section> -->
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<h2 class="title is-3 has-text-centered">BibTeX</h2>
<pre><code>
@article{li2024decisionnce,
title={DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning},
author={Li, Jianxiong and Zheng, Jinliang and Zheng, Yinan and Mao, Liyuan and Hu, Xiao and Cheng, Sijie and Niu, Haoyi and Liu, Jihao and Liu, Yu and Liu, Jingjing and others},
journal={arXiv preprint arXiv:2402.18137},
year={2024}
}
</code></pre>
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