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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="DLO Insertion">
<meta name="keywords" content="Reinforcement learning, robot learning">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>DLOinsert</title>
<script async src="https://www.googletagmanager.com/gtag/js?id=G-PYVRSFMDRL"></script>
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<script src="./static/js/index.js"></script>
<link href="offcanvas.css" rel="stylesheet">
</head>
<body>
<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-1 publication-title">
Learning for Deformable Linear Object Insertion Leveraging Flexibility
Estimation from Visual Cues
</div>
</div>
</div>
</div>
</div>
</section>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-13">Mingen Li and Changhyun Choi</h2>
<div class="content has-text-justified">
</div>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-2">Abstract</h2>
<div class="content has-text-justified">
<p>
Manipulation of deformable Linear objects (DLOs), including iron wire, rubber, silk, and nylon rope, is ubiquitous in daily life.
These objects exhibit diverse physical properties, such as Young's modulus and bending stiffness. Such diversity poses challenges for developing generalized manipulation policies.
However, previous research limited their scope to single-material DLOs and engaged in time-consuming data collection for the state estimation.
In this paper, we propose a two-stage manipulation approach consisting of a material property (e.g., flexibility) estimation and policy learning for DLO insertion with reinforcement learning. Firstly, we design a flexibility estimation scheme that characterizes the properties of different types of DLOs. The ground truth flexibility data is collected in simulation to train our flexibility estimation module.
During the manipulation, the robot interacts with the DLOs to estimate flexibility by analyzing their visual configurations.
Secondly, we train a policy conditioned on the estimated flexibility to perform challenging DLO insertion tasks.
Our pipeline trained with diverse insertion scenarios achieves an 85.6% success rate in simulation and 66.67% in real robot experiments.
</p>
</div>
</div>
</div>
</div>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-2">Video</h2>
<div class="content has-text-justified">
<video id="generalvideo" controls muted loop playsinline height="100%">
<source src="static/videos/finalicra.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
<br>
<br>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h3 class="title is-3">Simulation Experiments</h3>
<!-- <p>
We train dexterous manipulation using RL with single-viewed point cloud. The <span style="color: #3D85C6">blue points</span>
are observed by camera <br> while the <span style="color: #CC0000">red points</span> are imagined from robot
model.
</p> -->
<div class="content has-text-justified">
</div>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<p>
The following experiment shows the evaluation result for Ours w/ f for fix theta and random theta.
</p>
<div class="content has-text-justified">
</div>
</div>
</div>
</div>
<div class="container is-max-fullhd">
<div class="columns is-centered">
<div class="column is-one-second">
<!-- <h3 class="title is-4">Insertion</h3> -->
<img src="static/videos/fixobsstat_42000.gif" alt="this slowpoke moves" width="400" />
<br>
<br>
<br>
</div>
<div class="column is-one-second">
<img src="static/videos/randobsstat_50000.gif" alt="this slowpoke moves" width="400" />
<br>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-2">Real-World Experiments</h2>
<div class="content has-text-justified">
</div>
</div>
</div>
</div>
<br>
<br>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<!-- <p>
We train dexterous manipulation using RL with single-viewed point cloud. The <span style="color: #3D85C6">blue points</span>
are observed by camera <br> while the <span style="color: #CC0000">red points</span> are imagined from robot
model.
</p> -->
<div class="content has-text-justified">
</div>
</div>
</div>
</div>
<div class="container is-max-fullhd">
<div class="columns is-centered">
<div class="column is-one-second">
<!-- <h3 class="title is-4">Insertion</h3> -->
<video id="pivoting-1" controls muted loop autoplay playsinline height="100%">
<source src="static/videos/fixobs3rope4.mp4" type="video/mp4">
</video>
<br>
</div>
<div class="column is-one-second">
<video id="pivoting-3" controls muted loop autoplay playsinline height="100%">
<source src="static/videos/randobs2_up1.1420_rope5.mp4" type="video/mp4">
</video>
<br>
</div>
<div class="column is-one-second">
<video id="pivoting-3" controls muted loop autoplay playsinline height="100%">
<source src="static/videos/randobs2_down1.9778_rope1.mp4" type="video/mp4">
</video>
<br>
</div>
</div>
</div>
</body>
</html>