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Squirtleee merged 2 commits into from
Apr 25, 2026
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added pRRTC project website
Apr 25, 2026
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Update papers.bib to include link to pRRTC website
Squirtleee Apr 25, 2026
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1 change: 1 addition & 0 deletions _bibliography/papers.bib
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Expand Up @@ -32,6 +32,7 @@ @inproceedings{huangjadhav2026prrtc
pdf = {https://arxiv.org/abs/2503.06757},
code = {https://github.com/CoMMALab/pRRTC},
video = {https://www.youtube.com/watch?v=okpqftLB6P8},
website = {/papers/pRRTC},
projects = {realtime},
note = {To Appear},
abbr = {ICRA},
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284 changes: 284 additions & 0 deletions papers/pRRTC/index.html
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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning">
<meta name="keywords" content="pRRTC, Manipulation, GPU, Planning, Robotics">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning</title>

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More Research
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<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">pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/Squirtleee">Chih H. Huang*</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://commalab.org/members/jadhav/">Pranav Jadhav*</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://brianplancher.com/">Brian Plancher</a><sup>3</sup>,</span>
and
<span class="author-block">
<a href="https://commalab.org/members/kingston/">Zachary Kingston</a><sup>2</sup>
</span>
</div>
<sup>1</sup>Columbia University&nbsp;&nbsp;&nbsp;&nbsp;
<sup>2</sup>Purdue University&nbsp;&nbsp;&nbsp;&nbsp;
<sup>3</sup>Barnard College and Dartmouth College

<div class="is-size-5 publication-authors">
<span class="author-block">* equal contribution</span>
</div>

<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
<span class="link-block">
<a href="https://arxiv.org/abs/2503.06757"
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>Paper</span>
</a>
</span>
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<a href="https://arxiv.org/abs/2503.06757"
class="external-link button is-normal is-rounded is-dark">
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<span>arXiv</span>
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<a href="https://github.com/CoMMALab/pRRTC"
class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
</a>
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</div>

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</div>
</section>

<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video id="teaser" autoplay muted loop playsinline height="100%">
<source src="./static/videos/demo.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
pRRTC finds collision-free paths in milliseconds, even for high dimensional systems <br/>
[deployed on a 14 degree of freedom dual Franka]
</h2>
</div>
</div>
</section>

<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Sampling-based motion planning algorithms, like the Rapidly-Exploring Random Tree (RRT) and its widely
used variant, RRT-Connect, provide efficient solutions for high-dimensional planning problems faced by real-world robots.
However, these methods remain computationally intensive, particularly in complex environments that require many collision
checks. To improve performance, recent efforts have explored parallelizing specific components of RRT such as collision
checking, or running multiple planners independently. However, little has been done to develop an integrated parallelism
approach, co-designed for large-scale parallelism. </p>
<p>
In this work we present pRRTC, a RRT-Connect based planner co-designed
for GPU acceleration across the entire algorithm through parallel expansion and SIMT-optimized collision checking. We
evaluate the effectiveness of pRRTC on the MotionBenchMaker dataset using robots with 7, 8, and 14 degrees of freedom
(DoF). Compared to the state-of-the-art, pRRTC achieves as much as a 10× speedup on constrained reaching tasks with
a 5.4× reduction in standard deviation. pRRTC also achieves a 1.4× reduction in average initial path cost. Finally, we
deploy pRRTC on a 14-DoF dual Franka Panda arm setup and demonstrate real-time, collision-free motion planning with
dynamic obstacles. We open-source our planner to support the wider community. </p>
</div>
</div>
</div>
<!--/ Abstract. -->


<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column is-full-width">
<h3 class="title is-4">Three-levels of GPU-Parallelism for RRT-Connect</h3>
<div class="content has-text-justified">
<p>
On the high-level, pRRTC constructs two search trees in parallel. At the mid-level, pRRTC runs hundreds of parallel RRT-Connect iterations asynchronously across both trees.
At the low-level, pRRTC parallelizes forward kinematics tracing, discretized edge collision checking, and nearest neighbors search.
</p>
<details style="background-color: #e8f4fc; padding: 2rem; border-radius: 12px;">
<summary>Design Visualization</summary>
<img src="./static/result/prrtc_design.png" class="prrtc_design" alt="Design of pRRTC."/>
</details>
<br/>
<p>
When benchmarked against the CPU-based SIMD-accelerated VAMP-RRTC, pRRTC achieves as much as a 10x speedup in planning time
<i>(left)</i>. pRRTC also shows increasing benefit for systems of higher dimension <i>(right)</i>.
</p>
<details style="background-color: #e8f4fc; padding: 2rem; border-radius: 12px;">
<summary>Benchmark Plots</summary>
<img src="./static/result/pRRTC_result.png" alt="pRRTC performance plots"/>
</details>
</div>
</div>
</div>
</div>
</section>


<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX (preprint)</h2>
<pre><code>@article{huang2025prrtc,
title={prrtc: Gpu-parallel rrt-connect for fast, consistent, and low-cost motion planning},
author={Huang, Chih H and Jadhav, Pranav and Plancher, Brian and Kingston, Zachary},
journal={arXiv preprint arXiv:2503.06757},
year={2025}
}

</code></pre>
</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">Post submission: <br/> pRRTC-inspired asymptotically optimal planner</h2>
<div class="content has-text-justified">
<p>
pRRTC has been paired with the AO-X meta-algorithm for almost-surely asymptotically-optimal planning.
We plan to release the planner in September 2026.</p>
</p>
<details style="background-color: #e8f4fc; padding: 2rem; border-radius: 12px;">
<summary>Result Preview</summary>
<p>
The pRRTC-based optimizing planner has been tentatively named pAORRTC,
and this is in contrast with the CPU-based SIMD-accelerated <a href="https://arxiv.org/abs/2505.10542">AORRTC</a> planner (which will also be at ICRA 2026!).
</p><p>
<b>Result 1: pAORRTC vs AORRTC in terms of time to initial solution on the 8-DoF Fetch MotionBenchMaker dataset.</b>
</p>
<img src="./static/result/fetch_init.png" class="result_asao_init" alt="Result of AO-X pRRTC initial solve time."/> <br/> <br/>
<p>
<b>Result 2: pAORRTC vs AORRTC in terms of optimization time and path cost on the 8-DoF Fetch MotionBenchMaker dataset.</b><br/> <br/>
<img src="./static/result/fetch_avg.png" class="result_asao_optimize" alt="Result of AO-X pRRTC optimizing time and path cost."/>
</p>
</details>
</div>
</div>
</div>


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This means you are free to borrow the <a
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