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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="description" content="RPMArt" />
<meta
name="keywords"
content="RPMArt, Robotics, Robot Learning, Articulation, Robustness, Sim2Real"
/>
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<!-- <title>RPMArt: Towards Robust Perception and Manipulation for Articulated Objects</title> -->
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role="button"
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aria-label="menu"
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<a class="navbar-item" href="https://r-pmart.github.io/">
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<div class="navbar-item has-dropdown is-hoverable">
<a class="navbar-link"> Related Work </a>
<div class="navbar-dropdown">
<a class="navbar-item" href="https://github.com/dadadadawjb/A-PIE"> A-PIE </a>
<a class="navbar-item" href="https://sites.google.com/view/gamma-articulation"> GAMMA </a>
</div>
</div>
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</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-1 publication-title">
<span class="highlight">RPMArt</span
><span class="lowlight">: Towards </span>
<span class="highlight">R</span
><span class="lowlight">obust </span>
<span class="highlight">P</span
><span class="lowlight">erception and </span>
<span class="highlight">M</span
><span class="lowlight">anipulation for </span>
<span class="highlight">Art</span
><span class="lowlight">iculated Objects</span>
</h1>
<h4 class="title is-4 conference"><a href="https://iros2024-abudhabi.org/" class="conference">IEEE/RSJ International Conference on Intelligent Robots and Systems (<span class="grad_text">IROS</span>) 2024</a></h4>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://dadadadawjb.github.io/">Junbo Wang</a
><sup>1</sup>,
</span>
<span class="author-block">
<a href="mailto:[email protected]">Wenhai Liu</a
><sup>1</sup>,
</span>
<span class="author-block">
<a href="mailto:[email protected]">Qiaojun Yu</a
><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://qq456cvb.github.io/">Yang You</a
><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://liuliu66.github.io/">Liu Liu</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="mailto:[email protected]">Weiming Wang</a
><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://www.mvig.org/">Cewu Lu</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<sup>1</sup
><span class="author-block">Shanghai Jiao Tong University</span
>, <sup>2</sup
><span class="author-block">Stanford University</span>,
<sup>3</sup
><span class="author-block"
>Hefei University of Technology</span
>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a
href="https://arxiv.org/abs/2403.16023"
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://rasevents.org/presentation?id=144610"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fas fa-file-image"></i>
</span>
<span>Poster</span>
</a>
</span>
<span class="link-block">
<a
href="https://youtu.be/7yc26ZQcpc0"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fab fa-youtube"></i>
</span>
<span>Video</span>
</a>
</span>
<span class="link-block">
<a
href="https://github.com/R-PMArt/rpmart"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<span class="link-block">
<a
href="https://huggingface.co/dadadadawjb/RoArtNet"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fa fa-sitemap"></i>
</span>
<span>Model</span>
</a>
</span>
<span class="link-block">
<a
href="https://huggingface.co/datasets/dadadadawjb/RealArt-6"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fa fa-database"></i>
</span>
<span>Data</span>
</a>
</span>
</div>
</div>
</div>
</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-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Articulated objects are commonly found in daily life. It is
essential that robots can exhibit robust perception and
manipulation skills for articulated objects in real-world
robotic applications. However, existing methods for articulated
objects insufficiently address noise in point clouds and
struggle to bridge the gap between simulation and reality, thus
limiting the practical deployment in real-world scenarios. To
tackle these challenges, we propose a framework towards
<b>R</b>obust <b>P</b>erception and <b>M</b>anipulation for
<b>Art</b>iculated Objects (<b>RPMArt</b>), which learns to
estimate the articulation parameters and manipulate the
articulation part from the noisy point cloud. Our primary
contribution is a <b>Ro</b>bust <b>Art</b>iculation
<b>Net</b>work (<b>RoArtNet</b>) that is able to predict both
joint parameters and affordable points robustly by local feature
learning and point tuple voting. Moreover, we introduce an
articulation-aware classification scheme to enhance its ability
for sim-to-real transfer. Finally, with the estimated affordable
point and articulation joint constraint, the robot can generate
robust actions to manipulate articulated objects. After learning
only from synthetic data, RPMArt is able to transfer zero-shot
to real-world articulated objects. Experimental results confirm
our approach's effectiveness, with our framework achieving
state-of-the-art performance in both noise-added simulation and
real-world environments.
</p>
</div>
</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-3">Video</h2>
<div class="publication-video">
<iframe
src="https://www.youtube.com/embed/7yc26ZQcpc0?si=yVclyJ8Drl-8G7YP?rel=0&showinfo=0"
title="YouTube video player"
frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen
></iframe>
</div>
</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-3">Framework</h2>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<div class="column content has-text-justified">
<ul>
<li><b>Robust articulation network</b></li>
<ul>
<li>Input: single-view point cloud</li>
<li>Output: joint parameters and affordable point</li>
</ul>
<li><b>Affordance-based physics-guided manipulation</b></li>
<ul>
<li>Affordable grasp pose selection</li>
<li>Articulation joint constraint</li>
</ul>
</ul>
<br />
<p>
During training, several voting targets are generated by part
segmentation, joint parameters and affordable points from the
simulator to supervise RoArtNet. When given the real-world noisy
point cloud observation, RoArtNet can still generate robust
joint parameters and affordable points estimation by point tuple
voting. Then, affordable initial grasp poses can be selected
from AnyGrasp-generated grasp poses based on the estimated
affordable points, and subsequent actions can be constrained by
the estimated joint parameters.
</p>
</div>
</div>
<div class="column has-text-centered">
<div class="content">
<img
src="./static/images/framework.png"
alt="Framework"
style="width: 100%"
/>
</div>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content">
<img
src="./static/images/roartnet.png"
alt="RoArtNet"
style="width: 100%"
/>
<p>
Our primary contribution is the robust articulation network,
which is carefully designed to be <b>robust</b> and <b>sim-to-real</b>,
by <b>local feature learning</b>, <b>point tuple voting</b>, and an <b>articulation awareness</b> scheme.
First, a collection of point tuples are uniformly sampled from the point cloud.
For each point tuple, we predict several voting targets with a neural network from the local context features of the point tuple.
Further, an articulation score is applied to supervise the neural network so that the network is aware of the articulation structure.
Then, we can generate multiple candidates using the predicted voting targets, given the one degree-of-freedom ambiguity constraint.
The candidate joint origin, joint direction and affordable point with the most votes,
from only point tuples with high articulation score, are selected as the final estimation.
</p>
</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-3">Experimental Results</h2>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content">
<p>
We evaluate RPMArt in both noise-added simulation and real-world environments.
In the simulation environment, we add different levels of noise to the point clouds to test the robustness of models.
And we also test the sim-to-real transfer ability of RPMArt by directly applying the model trained on synthetic data to real-world scenarios.
</p>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h3 class="title is-4">Simulation Perception</h3>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content">
<img
src="./static/images/sim_perception_origin_all.png"
alt="sim_perception_origin_all"
style="width: 100%"
/>
<br />
<p align="center">Joint origin estimation results</p>
</div>
<div class="column content">
<img
src="./static/images/sim_perception_direction_all.png"
alt="sim_perception_direction_all"
style="width: 100%"
/>
<br />
<p align="center">Joint direction estimation results</p>
</div>
<div class="column content">
<img
src="./static/images/sim_perception_affordance_all.png"
alt="sim_perception_affordance_all"
style="width: 100%"
/>
<br />
<p align="center">Affordable point estimation results</p>
</div>
</div>
<p>
We gradually add higher level of noise to the input point clouds,
and test the joint parameters and affordable points estimation performance.
Lower is better.
Error bars represent the standard deviation.
Results are averaged across six object categories.
This shows that RoArtNet is robust to noise in the input point clouds.
</p>
<br />
<p>
<details>
<summary><b>Results for each object category.</b></summary>
<br />
<table class="table is-striped is-fullwidth">
<thead>
<tr>
<th>Category</th>
<th>Joint origin</th>
<th>Joint direction</th>
<th>Affordable point</th>
</tr>
</thead>
<tbody>
<tr>
<td>Microwave</td>
<td>
<img
src="./static/images/sim_perception_origin_microwave.png"
alt="sim_perception_origin_microwave"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_microwave.png"
alt="sim_perception_direction_microwave"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_microwave.png"
alt="sim_perception_affordance_microwave"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Refrigerator</td>
<td>
<img
src="./static/images/sim_perception_origin_refrigerator.png"
alt="sim_perception_origin_refrigerator"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_refrigerator.png"
alt="sim_perception_direction_refrigerator"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_refrigerator.png"
alt="sim_perception_affordance_refrigerator"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Safe</td>
<td>
<img
src="./static/images/sim_perception_origin_safe.png"
alt="sim_perception_origin_safe"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_safe.png"
alt="sim_perception_direction_safe"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_safe.png"
alt="sim_perception_affordance_safe"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Storage Furniture</td>
<td>
<img
src="./static/images/sim_perception_origin_storagefurniture.png"
alt="sim_perception_origin_storagefurniture"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_storagefurniture.png"
alt="sim_perception_direction_storagefurniture"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_storagefurniture.png"
alt="sim_perception_affordance_storagefurniture"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Drawer</td>
<td>
<img
src="./static/images/sim_perception_origin_drawer.png"
alt="sim_perception_origin_drawer"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_drawer.png"
alt="sim_perception_direction_drawer"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_drawer.png"
alt="sim_perception_affordance_drawer"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Washing Machine</td>
<td>
<img
src="./static/images/sim_perception_origin_washingmachine.png"
alt="sim_perception_origin_washingmachine"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_direction_washingmachine.png"
alt="sim_perception_direction_washingmachine"
style="width: 100%"
/>
</td>
<td>
<img
src="./static/images/sim_perception_affordance_washingmachine.png"
alt="sim_perception_affordance_washingmachine"
style="width: 100%"
/>
</td>
</tr>
</tbody>
</table>
<p>
Results show that all baselines and RoArtNet achieve high estimation precision without noise added across all object categories.
Nevertheless, with the increasing level of noise,
all three baselines exhibit a pronounced increase in estimation errors,
while the mean estimation error of RoArtNet increases very slowly.
And the baselines also have much higher standard deviation compared to RoArtNet when high level of noise is added.
There also exist some interesting phenomena in the results.
The StorageFurniture and Drawer categories have much higher estimation errors for ANCSH and GAMMA compared to other categories.
This is possibly because the StorageFurniture and Drawer categories comprise relatively small articulation parts,
while the ANCSH and GAMMA models somehow rely on the part segmentation to finish the estimation.
In addition, the two methods both contain an optimization-based procedure,
i.e. RANSAC transformation estimation for ANCSH and DBSCAN part clustering for GAMMA,
which may be unsolvable when the part segmentation is not accurate.
It is also interesting that the WashingMachine category demonstrates partly a decreasing trend when the noise level is high.
</p>
</details>
</p>
<br />
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h3 class="title is-4">Simulation Manipulation</h3>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content">
<p>
We also add different levels of noise to the input point clouds,
and report the manipulation success rates.
We run around 100 trials per object instance for each task.
Higher is better.
Results are averaged across six tasks.
We also run manipulation experiments using the ground truth joint parameters and affordable points,
and the average success rates are 96.694% and 99.627% for pulling and pushing respectively.
It demonstrates that RPMArt can still stably manipulate articulated objects with inaccurate perception results.
We also show the twelve task examples finished by RPMArt under noise level 2.
We can observe that the perception results are still robust to noise but actually not perfect,
but RPMArt can still successfully manipulate the articulated objects.
</p>
</div>
<div class="column content">
<img
src="./static/images/sim_manipulation_all.png"
alt="sim_manipulation_all"
style="width: 100%"
/>
<br />
<p align="center">Manipulation results</p>
</div>
</div>
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content">
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_microwave_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull Microwave</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_refrigerator_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull Refrigerator</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagefurniture_prismatic_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull StorageFurniture (Prismatic)</p>
</div>
<div class="column content">
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_safe_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull Safe</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagefurniture_revolute_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull StorageFurniture (Revolute)</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagev2furniture_pull.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Pull Drawer</p>
</div>
<div class="column content">
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_microwave_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push Microwave</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_refrigerator_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push Refrigerator</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagefurniture_prismatic_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push StorageFurniture (Prismatic)</p>
</div>
<div class="column content">
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_safe_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push Safe</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagefurniture_revolute_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push StorageFurniture (Revolute)</p>
<video controls autoplay muted loop style="width: 100%">
<source
src="./static/videos/sim_storagev2furniture_push.mp4"
type="video/mp4"
/>
Your browser does not support the video tag.
</video>
<br />
<p align="center">Push Drawer</p>
</div>
</div>
<!-- <br /> -->
<p>
<details>
<summary><b>Results for each task.</b></summary>
<br />
<table class="table is-striped is-fullwidth">
<thead>
<tr>
<th>Tasks</th>
<th>Results</th>
<th>Tasks</th>
<th>Results</th>
</tr>
</thead>
<tbody>
<tr>
<td>Pull/Push Microwave</td>
<td>
<img
src="./static/images/sim_manipulation_microwave.png"
alt="sim_manipulation_microwave"
style="width: 100%"
/>
</td>
<td>Pull/Push Refrigerator</td>
<td>
<img
src="./static/images/sim_manipulation_refrigerator.png"
alt="sim_manipulation_refrigerator"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Pull/Push Safe</td>
<td>
<img
src="./static/images/sim_manipulation_safe.png"
alt="sim_manipulation_safe"
style="width: 100%"
/>
</td>
<td>Pull/Push StorageFurniture (Revolute)</td>
<td>
<img
src="./static/images/sim_manipulation_storagefurniture_revolute.png"
alt="sim_manipulation_storagefurniture_revolute"
style="width: 100%"
/>
</td>
</tr>
<tr>
<td>Pull/Push StorageFurniture (Prismatic)</td>
<td>
<img
src="./static/images/sim_manipulation_storagefurniture_prismatic.png"
alt="sim_manipulation_storagefurniture_prismatic"
style="width: 100%"
/>
</td>
<td>Pull/Push Drawer</td>
<td>
<img
src="./static/images/sim_manipulation_drawer.png"
alt="sim_manipulation_drawer"
style="width: 100%"
/>
</td>
</tr>
</tbody>
</table>
<p>
Results show that RPMArt achieves the highest success rate under noise level 4 across almost all tasks except Push Refrigerator.
And we can observe the least degradation in performance of RPMArt with the increase of noise.
Actually, when no noise is added, RPMArt only achieves comparable or even slightly worse performance than the baselines,
especially GAMMA.
There also exist some interesting phenomena in the results.
Under noise level 4, PointNet++-based method always achieves better performance than ANCSH and GAMMA in pushing tasks,
while ANCSH and GAMMA often have better results in pulling tasks.
Note that pulling tasks are often considered more difficult than pushing tasks,
since pulling tasks often require first grasping the target part while it is unnecessary for pushing tasks.
However, ANCSH and GAMMA often obtain higher success rates than PointNet++-based method without noise added.
In addition, for Pull/Push StorageFurniture (Prismatic) and Pull/Push Drawer under noise level 4,
ANCSH and GAMMA achieve much lower success rates,
which may also be attributed to the fact that these parts are relatively small,
as discussed in simulation perception.
</p>
</details>
</p>
<br />
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h3 class="title is-4">Real-world Perception</h3>
</div>
</div>
</div>
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered has-text-justified">
<div class="column content is-four-fifths">
<table class="table is-striped is-fullwidth">
<thead>
<tr>
<th rowspan="2">Category</th>
<th rowspan="2">Method</th>
<th colspan="3">Error</th>
</tr>
<tr>
<td>Orig. (cm)</td>
<td>Dir. (<sup>o</sup>)</td>
<td>Afford. (cm)</td>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="4">Microwave</td>
<td>PointNet++</td>
<td>4.495<font size="1">±3.573</font></td>
<td>9.273<font size="1">±5.828</font></td>
<td>15.443<font size="1">±4.730</font></td>
</tr>
<tr>
<td>ANCSH</td>
<td>5.103<font size="1">±5.522</font></td>
<td>9.166<font size="1">±9.557</font></td>
<td>12.711<font size="1">±7.927</font></td>
</tr>
<tr>
<td>GAMMA</td>
<td><b>2.531</b><font size="1">±2.901</font></td>
<td>9.911<font size="1">±10.671</font></td>
<td>7.242<font size="1">±10.191</font></td>
</tr>
<tr>
<td>RoArtNet (ours)</td>
<td>3.830<font size="1">±<b>2.372</b></font></td>
<td><b>5.189</b><font size="1">±<b>3.619</b></font></td>
<td><b>6.754</b><font size="1">±<b>3.275</b></font></td>
</tr>
<tr>
<td rowspan="4">Refrigerator</td>
<td>PointNet++</td>
<td>5.210<font size="1">±4.274</font></td>
<td>9.605<font size="1">±5.340</font></td>
<td>12.475<font size="1">±9.505</font></td>
</tr>
<tr>
<td>ANCSH</td>
<td>5.938<font size="1">±5.798</font></td>
<td><b>7.998</b><font size="1">±5.910</font></td>
<td>12.814<font size="1">±13.604</font></td>
</tr>
<tr>
<td>GAMMA</td>
<td>4.019<font size="1">±4.580</font></td>
<td>8.684<font size="1">±6.455</font></td>
<td>12.331<font size="1">±9.974</font></td>
</tr>
<tr>
<td>RoArtNet (ours)</td>
<td><b>2.111</b><font size="1">±<b>1.701</b></font></td>
<td>8.491<font size="1">±<b>4.270</b></font></td>
<td><b>5.849</b><font size="1">±<b>2.797</b></font></td>
</tr>
<tr>
<td rowspan="4">Safe</td>
<td>PointNet++</td>
<td>5.985<font size="1">±4.162</font></td>
<td>5.936<font size="1">±2.861</font></td>
<td>9.235<font size="1">±5.630</font></td>
</tr>
<tr>
<td>ANCSH</td>
<td>5.167<font size="1">±6.758</font></td>
<td>7.706<font size="1">±14.275</font></td>
<td>8.505<font size="1">±9.768</font></td>
</tr>
<tr>
<td>GAMMA</td>
<td><b>3.179</b><font size="1">±3.857</font></td>
<td>8.156<font size="1">±13.737</font></td>
<td>9.062<font size="1">±9.667</font></td>
</tr>
<tr>
<td>RoArtNet (ours)</td>
<td>4.116<font size="1">±<b>2.428</b></font></td>
<td><b>5.878</b><font size="1">±<b>2.769</b></font></td>
<td><b>8.349</b><font size="1">±<b>4.391</b></font></td>
</tr>
<tr>
<td rowspan="4">StorageFurniture</td>
<td>PointNet++</td>
<td>7.542<font size="1">±4.517</font></td>
<td><b>8.776</b><font size="1">±<b>4.991</b></font></td>
<td>10.634<font size="1">±4.025</font></td>
</tr>
<tr>
<td>ANCSH</td>
<td>6.408<font size="1">±4.222</font></td>
<td>9.612<font size="1">±6.404</font></td>
<td>5.176<font size="1">±6.023</font></td>
</tr>
<tr>
<td>GAMMA</td>
<td><b>3.481</b><font size="1">±2.275</font></td>
<td>12.672<font size="1">±10.186</font></td>
<td><b>4.742</b><font size="1">±6.664</font></td>
</tr>
<tr>
<td>RoArtNet (ours)</td>
<td>4.604<font size="1">±<b>2.050</b></font></td>
<td>9.682<font size="1">±5.449</font></td>
<td>7.946<font size="1">±<b>3.402</b></font></td>
</tr>
<tr>
<td rowspan="4">Drawer</td>
<td>PointNet++</td>
<td>8.331<font size="1">±3.377</font></td>
<td><b>7.862</b><font size="1">±<b>5.295</b></font></td>
<td>10.227<font size="1">±4.465</font></td>
</tr>
<tr>
<td>ANCSH</td>
<td>13.849<font size="1">±3.764</font></td>
<td>12.143<font size="1">±8.030</font></td>