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<!DOCTYPE html> <html lang="en"> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <title> Publications | CoMMA Lab @ Purdue </title> <meta name="author" content=" "> <meta name="description" content="The Computational Robot Motion and Autonomy Lab at Purdue University "> <meta name="keywords" content="jekyll, jekyll-theme, academic-website, portfolio-website"> <link rel="stylesheet" href="/assets/css/bootstrap.min.css?a4b3f509e79c54a512b890d73235ef04"> <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/mdbootstrap@4.20.0/css/mdb.min.css" integrity="sha256-jpjYvU3G3N6nrrBwXJoVEYI/0zw8htfFnhT9ljN3JJw=" crossorigin="anonymous"> <link defer rel="stylesheet" href="/assets/css/academicons.min.css?f0b7046b84e425c55f3463ac249818f5"> <link defer rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700|Roboto+Slab:100,300,400,500,700|Material+Icons&display=swap"> <link defer rel="stylesheet" href="/assets/css/jekyll-pygments-themes-github.css?591dab5a4e56573bf4ef7fd332894c99" media="" id="highlight_theme_light"> <link rel="shortcut icon" href="data:image/svg+xml,<svg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20100%20100%22><text%20y=%22.9em%22%20font-size=%2290%22>%F0%9F%A6%BE</text></svg>"> <link rel="stylesheet" href="/assets/css/main.css?d41d8cd98f00b204e9800998ecf8427e"> <link rel="canonical" href="https://commalab.github.io/publications/"> <script src="/assets/js/theme.js?ca131c86afeddc68f0e9d3278afbc9b8"></script> <link defer rel="stylesheet" href="/assets/css/jekyll-pygments-themes-native.css?5847e5ed4a4568527aa6cfab446049ca" media="none" id="highlight_theme_dark"> <script>initTheme();</script> </head> <body class="fixed-top-nav sticky-bottom-footer"> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top" role="navigation"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/"> CoMMA Lab @ Purdue </a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">About </a> </li> <li class="nav-item "> <a class="nav-link" href="/members">Team </a> </li> <li class="nav-item active"> <a class="nav-link" href="/publications/">Publications <span class="sr-only">(current)</span> </a> </li> <li class="nav-item "> <a class="nav-link" href="/projects/">Research </a> </li> <li class="nav-item "> <a class="nav-link" href="/repositories/">Code </a> </li> <li class="nav-item "> <a class="nav-link" href="/classes/">Teaching </a> </li> <li class="nav-item "> <a class="nav-link" href="/join">Join </a> </li> <li class="toggle-container"> <button id="light-toggle" title="Change theme"> <i class="ti ti-moon-filled" id="light-toggle-dark"></i> <i class="ti ti-sun-filled" id="light-toggle-light"></i> </button> </li> </ul> </div> </div> </nav> <progress id="progress" value="0"> <div class="progress-container"> <span class="progress-bar"></span> </div> </progress> </header> <div class="container mt-5" role="main"> <div class="post"> <header class="post-header"> <h1 class="post-title">Publications</h1> <p class="post-description"></p> </header> <article> <div class="publications"> * indicates equal contribution. <h2 class="bibliography">2025</h2> <ol class="bibliography"> <li> <div class="row"> <a name="huangjadhav2025prrtc" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#b31b1b"> <div> arXiv </div> </abbr> </div> <div class="col-sm-10"> <div class="title">pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning</div> <div class="author"> Chih H. Huang<sup>*</sup>, <a href="/members/jadhav">Pranav Jadhav<sup>*</sup></a>, <a href="https://brianplancher.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Brian Plancher</a>, and <a href="/members/kingston">Zachary Kingston</a> </div> <div class="periodical"> </div> <div class="periodical"> Under Review </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2503.06757" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/CoMMALab/pRRTC" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <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. As such, to improve performance, recent efforts have explored parallelizing specific components of RRT, such as collision checking or running multiple planners independently, but no prior work has integrated parallelism at multiple levels of the algorithm for robotic manipulation. In this work, we present pRRTC, a GPU-accelerated implementation of RRT-Connect that achieves parallelism across the entire algorithm through multithreaded expansion and connection, SIMT-optimized collision checking, and hierarchical parallelism optimization, improving efficiency, consistency, and initial solution cost. We evaluate the effectiveness of pRRTC on the MotionBenchMaker dataset using robots with 7, 8, and 14 degrees-of-freedom, demonstrating up to 6x average speedup on constrained reaching tasks at high collision checking resolution compared to state-of-the-art. pRRTC also demonstrates a 5x reduction in solution time variance and 1.5x improvement in initial path costs compared to state-of-the-art motion planners in complex environments across all robots.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@misc</span><span class="p">{</span><span class="nl">huangjadhav2025prrtc</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{{pRRTC}: {GPU}-Parallel {RRT}-Connect for Fast, Consistent, and Low-Cost Motion Planning}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Huang, Chih H. and Jadhav, Pranav and Plancher, Brian and Kingston, Zachary}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2503.06757}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{Under Review}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="coumar2025foam" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#b31b1b"> <div> arXiv </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Foam: A Tool for Spherical Approximation of Robot Geometry</div> <div class="author"> Sai Coumar, <a href="/members/chang">Gilbert Chang</a>, <a href="/members/kodkani">Nihar Kodkani</a>, and <a href="/members/kingston">Zachary Kingston</a> </div> <div class="periodical"> </div> <div class="periodical"> Under Review </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2503.13704" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/CoMMALab/foam" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>Many applications in robotics require primitive spherical geometry, especially in cases where efficient distance queries are necessary. Manual creation of spherical models is time-consuming and prone to errors. This paper presents Foam, a tool to generate spherical approximations of robot geometry from an input Universal Robot Description Format (URDF) file. Foam provides a robust preprocessing pipeline to handle mesh defects and a number of configuration parameters to control the level and approximation of the spherization, and generates an output URDF with collision geometry specified only by spheres. We demonstrate Foam on a number of standard robot models on common tasks, and demonstrate improved collision checking and distance query performance with only a minor loss in fidelity compared to the true collision geometry. We release our tool as an open source Python library and containerized command-line application to facilitate adoption across the robotics community.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@misc</span><span class="p">{</span><span class="nl">coumar2025foam</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Foam: A Tool for Spherical Approximation of Robot Geometry}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Coumar, Sai and Chang, Gilbert and Kodkani, Nihar and Kingston, Zachary}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2503.13704}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{Under Review}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="chengao2025diffsim" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100">RoboSoft</abbr> <figure> <picture> <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/robosoft25-480.webp 480w,/assets/img/publication_preview/robosoft25-800.webp 800w,/assets/img/publication_preview/robosoft25-1400.webp 1400w," sizes="200px" type="image/webp"></source> <img src="/assets/img/publication_preview/robosoft25.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="robosoft25.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm-10"> <div class="title">Physics-Grounded Differentiable Simulation for Soft Growing Robots</div> <div class="author"> <a href="/members/lucas">Lucas Chen<sup>*</sup></a>, <a href="/members/yitian">Yitian Gao<sup>*</sup></a>, Sicheng Wang, Francesco Fuentes, <a href="https://engineering.purdue.edu/ME/People/ptProfile?resource_id=241064" class="nonmember" rel="external nofollow noopener" target="_blank">Laura H. Blumenschein</a>, and <a href="/members/kingston">Zachary Kingston</a> </div> <div class="periodical"> <em>In IEEE-RAS International Conference on Soft Robotics</em> </div> <div class="periodical"> To Appear </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2501.17963" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/CoMMALab/DiffVineSimPy" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p> Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles for autonomous operation and design optimization. Although there exist simulators for these systems that have achieved qualitative and quantitative success in matching high-level behavior, they still often fail to capture realistic vine robot shapes using simplified parameter models and have difficulties in high-throughput simulation necessary for planning and parameter optimization. We propose a differentiable simulator for these systems, enabling the use of the simulator "in-the-loop" of gradient-based optimization approaches to address the issues listed above. With the more complex parameter fitting made possible by this approach, we experimentally validate and integrate a closed-form nonlinear stiffness model for thin-walled inflated tubes based on a first-principles approach to local material wrinkling. Our simulator also takes advantage of data-parallel operations by leveraging existing differentiable computation frameworks, allowing multiple simultaneous rollouts. We demonstrate the feasibility of using a physics-grounded nonlinear stiffness model within our simulator, and how it can be an effective tool in sim-to-real transfer. We provide our implementation open source.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">chengao2025diffsim</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Physics-Grounded Differentiable Simulation for Soft Growing Robots}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Chen, Lucas and Gao, Yitian and Wang, Sicheng and Fuentes, Francesco and Blumenschein, Laura H. and Kingston, Zachary}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE-RAS International Conference on Soft Robotics}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2501.17963}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{To Appear}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="wilson2024fcit" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)</div> <div class="author"> Tyler S. Wilson, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a>, and <a href="https://robotic-esp.com/people/gammell/" class="nonmember" rel="external nofollow noopener" target="_blank">Jonathan D. Gammell</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> To Appear </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2411.17902" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://www.youtube.com/watch?v=Lb_5Znpcleg" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://robotic-esp.com/code/fcitstar/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations are local motion validation and querying the nearest neighbours of a configuration. Recent advances have significantly reduced the cost of motion validation by using single instruction/multiple data (SIMD) parallelism to improve solution times for satisficing motion planning problems. These advances have not yet been applied to asymptotically optimal motion planning. This paper presents Fully Connected Informed Trees (FCIT*), the first fully connected, informed, anytime almost-surely asymptotically optimal (ASAO) algorithm. FCIT* exploits the radically reduced cost of edge evaluation via SIMD parallelism to build and search fully connected graphs. This removes the need for nearest-neighbours structures, which are a dominant cost for many sampling-based motion planners, and allows it to find initial solutions faster than state-of-the-art ASAO (VAMP, OMPL) and satisficing (OMPL) algorithms on the MotionBenchMaker dataset while converging towards optimal plans in an anytime manner.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">wilson2024fcit</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees ({FCIT}*)}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Wilson, Tyler S. and Thomason, Wil and Kingston, Zachary and Kavraki, Lydia E. and Gammell, Jonathan D.}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2411.17902}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{To Appear}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="agrawal2024cnkz" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Constrained Nonlinear Kaczmarz Projection on Intersections of Manifolds for Coordinated Multi-Robot Mobile Manipulation</div> <div class="author"> <a href="https://www.akshayaagrawal.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Akshaya Agrawal</a>, Parker Mayer, <a href="/members/kingston">Zachary Kingston</a>, and <a href="https://engineering.oregonstate.edu/people/geoff-hollinger" class="nonmember" rel="external nofollow noopener" target="_blank">Geoffrey A. Hollinger</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> To Appear </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2410.21630" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/JBVAkshaya/PlanningOnManifoldIntersection" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>Cooperative manipulation tasks impose various structure-, task-, and robot-specific constraints on mobile manipulators. However, current methods struggle to model and solve these myriad constraints simultaneously. We propose a twofold solution: first, we model constraints as a family of manifolds amenable to simultaneous solving. Second, we introduce the constrained nonlinear Kaczmarz (cNKZ) projection technique to produce constraint-satisfying solutions. Experiments show that cNKZ dramatically outperforms baseline approaches, which cannot find solutions at all. We integrate cNKZ with a sampling-based motion planning algorithm to generate complex, coordinated motions for 3 to 6 mobile manipulators (18–36 DoF), with cNKZ solving up to 80 nonlinear constraints simultaneously and achieving up to a 92% success rate in cluttered environments. We also demonstrate our approach on hardware using three Turtlebot3 Waffle Pi robots with OpenMANIPULATOR-X arms.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">agrawal2024cnkz</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Constrained Nonlinear {Kaczmarz} Projection on Intersections of Manifolds for Coordinated Multi-Robot Mobile Manipulation}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Agrawal, Akshaya and Mayer, Parker and Kingston, Zachary and Hollinger, Geoffrey A.}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2410.21630}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{To Appear}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="guo2024castl" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">CaStL: Constraints as Specifications through LLM Translation for Long-Horizon Task and Motion Planning</div> <div class="author"> <a href="https://www.whguo.me/" class="nonmember" rel="external nofollow noopener" target="_blank">Weihang Guo</a>, <a href="/members/kingston">Zachary Kingston</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> To Appear </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2410.22225" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="abstract hidden"> <p>Large Language Models (LLMs) have demonstrated remarkable ability in long-horizon Task and Motion Planning (TAMP) by translating clear and straightforward natural language problems into formal specifications such as the Planning Domain Definition Language (PDDL). However, real-world problems are often ambiguous and involve many complex constraints. In this paper, we introduce Constraints as Specifications through LLMs (CaStL), a framework that identifies constraints such as goal conditions, action ordering, and action blocking from natural language in multiple stages. CaStL translates these constraints into PDDL and Python scripts, which are solved using an custom PDDL solver. Tested across three PDDL domains, CaStL significantly improves constraint handling and planning success rates from natural language specification in complex scenarios.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">guo2024castl</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{{CaStL}: Constraints as Specifications through {LLM} Translation for Long-Horizon Task and Motion Planning}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Guo, Weihang and Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2410.22225}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{To Appear}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="coumar2025ascii" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#b31b1b"> <div> arXiv </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Evaluating Machine Learning Approaches for ASCII Art Generation</div> <div class="author"> Sai Coumar and <a href="/members/kingston">Zachary Kingston</a> </div> <div class="periodical"> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2503.14375" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/saiccoumar/deep_ascii_converter" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>Generating structured ASCII art using computational techniques demands a careful interplay between aesthetic representation and computational precision, requiring models that can effectively translate visual information into symbolic text characters. Although Convolutional Neural Networks (CNNs) have shown promise in this domain, the comparative performance of deep learning architectures and classical machine learning methods remains unexplored. This paper explores the application of contemporary ML and DL methods to generate structured ASCII art, focusing on three key criteria: fidelity, character classification accuracy, and output quality. We investigate deep learning architectures, including Multilayer Perceptrons (MLPs), ResNet, and MobileNetV2, alongside classical approaches such as Random Forests, Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN), trained on an augmented synthetic dataset of ASCII characters. Our results show that complex neural network architectures often fall short in producing high-quality ASCII art, whereas classical machine learning classifiers, despite their simplicity, achieve performance similar to CNNs. Our findings highlight the strength of classical methods in bridging model simplicity with output quality, offering new insights into ASCII art synthesis and machine learning on image data with low dimensionality.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@misc</span><span class="p">{</span><span class="nl">coumar2025ascii</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Evaluating Machine Learning Approaches for {ASCII} Art Generation}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Coumar, Sai and Kingston, Zachary}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2025}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2503.14375}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.GR}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2024</h2> <ol class="bibliography"> <li> <div class="row"> <a name="liang2024ropras" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#e33900"> <a href="http://ifrr.org/isrr" rel="external nofollow noopener" target="_blank"> ISRR </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling</div> <div class="author"> Yuanchu Liang<sup>*</sup>, Edward Kim<sup>*</sup>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason<sup>*</sup></a>, <a href="/members/kingston">Zachary Kingston<sup>*</sup></a>, <a href="https://comp.anu.edu.au/people/hanna-kurniawati/" class="nonmember" rel="external nofollow noopener" target="_blank">Hanna Kurniawati</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In International Symposium of Robotics Research</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2411.07032" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="abstract hidden"> <p>Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., ≥15 steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space—a fundamental constraint for modern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon POMDPs, including on a problem with a planning horizon of more than 100 steps and a problem with a 15-dimensional state space that requires more than 20 look ahead steps. In all of these problems, ROP-RaS3 substantially outperforms other state-of-the-art methods by up to multiple folds.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">liang2024ropras</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Scaling Long-Horizon Online {POMDP} Planning via Rapid State Space Sampling}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Liang, Yuanchu and Kim, Edward and Thomason, Wil and Kingston, Zachary and Kurniawati, Hanna and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{International Symposium of Robotics Research}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="guo2024stac" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#b31b1b"> <div> arXiv </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation</div> <div class="author"> <a href="https://www.whguo.me/" class="nonmember" rel="external nofollow noopener" target="_blank">Weihang Guo</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://hangkaiyu.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Kaiyu Hang</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> </div> <div class="periodical"> Under Review </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2412.00366" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="abstract hidden"> <p>Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding random stops and coordination motion along each path) and generates paths that are more likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art baselines on challenging problems in manipulator cases.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@misc</span><span class="p">{</span><span class="nl">guo2024stac</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Guo, Weihang and Kingston, Zachary and Hang, Kaiyu and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">eprint</span> <span class="p">=</span> <span class="s">{2412.00366}</span><span class="p">,</span> <span class="na">archiveprefix</span> <span class="p">=</span> <span class="s">{arXiv}</span><span class="p">,</span> <span class="na">primaryclass</span> <span class="p">=</span> <span class="s">{cs.RO}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{Under Review}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="meng2024icra40" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#505050"> <div> Abstract </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Perception-aware Planning for Robotics: Challenges and Opportunities</div> <div class="author"> <a href="https://www.linkedin.com/in/qingxi-meng-0b733a125/" class="nonmember" rel="external nofollow noopener" target="_blank">Qingxi Meng</a>, <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://unhelkar.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Vaibhav Unhelkar</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://kavrakilab.org/publications/meng2024-review.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="abstract hidden"> <p>In this work, we argue that new methods are needed to generate robot motion for navigation or manipulation while effectively achieving perception goals. We support our argument by conducting experiments with a simulated robot that must accomplish a primary task, such as manipulation or navigation, while concurrently monitoring an object in the environment. Our preliminary study demonstrates that a decoupled approach fails to achieve high success in either action-focused motion generation or perception goals, motivating further developments of approaches that holistically consider both goals.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">meng2024icra40</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Unhelkar, Vaibhav and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Perception-aware Planning for Robotics: Challenges and Opportunities}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="ramsey2024" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA7900"> <a href="https://roboticsconference.org/" rel="external nofollow noopener" target="_blank"> RSS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking</div> <div class="author"> <a href="https://claytonwramsey.com" class="nonmember" rel="external nofollow noopener" target="_blank">Clayton W. Ramsey</a>, <a href="/members/kingston">Zachary Kingston<sup>*</sup></a>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason<sup>*</sup></a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In Robotics: Science and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2406.02807" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://www.youtube.com/watch?v=BzDKdrU1VpM" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://claytonwramsey.com/blog/captree" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Blog</a> <a href="https://github.com/kavrakilab/vamp" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.15607/RSS.2024.XX.038" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems such as manipulators, the most time-intensive component is collision checking. We present a novel spatial data structure, the collision-affording point tree (CAPT): an exact representation of point clouds that accelerates collision-checking queries between robots and point clouds by an order of magnitude, with an average query time of less than 10 nanoseconds on 3D scenes comprising thousands of points. With the CAPT, sampling-based planners can generate valid, high-quality paths in under a millisecond, with total end-to-end computation time faster than 60 FPS, on a single thread of a consumer-grade CPU. We also present a point cloud filtering algorithm, based on space-filling curves, which reduces the number of points in a point cloud while preserving structure. Our approach enables robots to plan at real-time speeds in sensed environments, opening up potential uses of planning for high-dimensional systems in dynamic, changing, and unmodeled environments.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">ramsey2024</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Ramsey, Clayton W. and Kingston, Zachary and Thomason, Wil and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Robotics: Science and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.15607/RSS.2024.XX.038}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="thomason2024vamp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Motions in Microseconds via Vectorized Sampling-Based Planning</div> <div class="author"> <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason<sup>*</sup></a>, <a href="/members/kingston">Zachary Kingston<sup>*</sup></a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/abs/2309.14545" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/kavrakilab/vamp" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/ICRA57147.2024.10611190" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom problems. This paper presents performance improvements of more than 500x over the state-of-the-art, bringing planning times into the range of microseconds and solution rates into the range of kilohertz, without specialized hardware. Our key insight is how to exploit fine-grained parallelism within sampling-based planners, providing generality-preserving algorithmic improvements to any such planner and significantly accelerating critical subroutines, such as forward kinematics and collision checking. We demonstrate our approach over a diverse set of challenging, realistic problems for complex robots ranging from 7 to 14 degrees-of-freedom. Moreover, we show that our approach does not require high-power hardware by also evaluating on a low-power single-board computer. The planning speeds demonstrated are fast enough to reside in the range of control frequencies and open up new avenues of motion planning research.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">thomason2024vamp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Thomason, Wil and Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Motions in Microseconds via Vectorized Sampling-Based Planning}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{8749--8756}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA57147.2024.10611190}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="quintero2024impdist" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> <figure> <picture> <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/impdist-480.webp 480w,/assets/img/publication_preview/impdist-800.webp 800w,/assets/img/publication_preview/impdist-1400.webp 1400w," sizes="200px" type="image/webp"></source> <img src="/assets/img/publication_preview/impdist.png" class="preview z-depth-1 rounded" width="100%" height="auto" alt="impdist.png" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm-10"> <div class="title">Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty</div> <div class="author"> <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="http://akyrillidis.github.io/about/" class="nonmember" rel="external nofollow noopener" target="_blank">Anastasios Kyrillidis</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/pdf/2309.16862.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/ICRA57147.2024.10610773" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Motion planning under sensing uncertainty is critical for robots in unstructured environments to guarantee safety for both the robot and any nearby humans. Most work on planning under uncertainty does not scale to high-dimensional robots such as manipulators, assumes simplified geometry of the robot or environment, or requires per-object knowledge of noise. Instead, we propose a method that directly models sensor-specific aleatoric uncertainty to find safe motions for high-dimensional systems in complex environments, without exact knowledge of environment geometry. We combine a novel implicit neural model of stochastic signed distance functions with a hierarchical optimization-based motion planner to plan low-risk motions without sacrificing path quality. Our method also explicitly bounds the risk of the path, offering trustworthiness. We empirically validate that our method produces safe motions and accurate risk bounds and is safer than baseline approaches.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">quintero2024impdist</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Quintero-Peña, Carlos and Thomason, Wil and Kingston, Zachary and Kyrillidis, Anastasios and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{2360--2367}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA57147.2024.10610773}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="elimelech2024skills" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Accelerating Long-Horizon Planning with Affordance-Directed Dynamic Grounding of Abstract Strategies</div> <div class="author"> <a href="http://khen.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Khen Elimelech</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason</a>, <a href="https://www.cs.rice.edu/~vardi/" class="nonmember" rel="external nofollow noopener" target="_blank">Moshe Y. Vardi</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://khen.io/icra24appendix.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/ICRA57147.2024.10610486" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Long-horizon task planning is important for robot autonomy, especially as a subroutine for frameworks such as Integrated Task and Motion Planning. However, task planning is computationally challenging and struggles to scale to realistic problem settings. We propose to accelerate task planning over an agent’s lifetime by integrating learned abstract strategies: a generalizable planning experience encoding introduced in earlier work. In this work, we contribute a practical approach to planning with strategies by introducing a novel formalism of planning in a skill-augmented domain. We also introduce and formulate the notion of a skill’s affordance, which indicates its predicted benefit to the solution, and use it to guide the planning and skill grounding processes. Together, our observations yield an affordance-directed, lazy-search planning algorithm, which can seamlessly compose strategies and actions to solve long-horizon planning problems. We evaluate our planner in an object rearrangement domain, where we demonstrate performance benefits relative to a state-of-the-art task planner.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">elimelech2024skills</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Elimelech, Khen and Kingston, Zachary and Thomason, Wil and Vardi, Moshe Y. and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Accelerating Long-Horizon Planning with Affordance-Directed Dynamic Grounding of Abstract Strategies}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{12688--12695}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA57147.2024.10610486}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="Goues2024" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics</div> <div class="author"> Claire Le Goues, Sebastian Elbaum, David Anthony, Z. Berkay Celik, Mauricio Castillo-Effen, Nikolaus Correll, Pooyan Jamshidi, Morgan Quigley, Trenton Tabor, and Qi Zhu </div> <div class="periodical"> </div> <div class="periodical"> Invited Contributor </div> <div class="links"> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/pdf/2401.12317.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@misc</span><span class="p">{</span><span class="nl">Goues2024</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Goues, Claire Le and Elbaum, Sebastian and Anthony, David and Celik, Z. Berkay and Castillo-Effen, Mauricio and Correll, Nikolaus and Jamshidi, Pooyan and Quigley, Morgan and Tabor, Trenton and Zhu, Qi}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{arXiv preprint arXiv:2401.12317}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">note</span> <span class="p">=</span> <span class="s">{Invited Contributor}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="Quintero2024wksp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#505050"> <div> Workshop </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty</div> <div class="author"> <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="http://akyrillidis.github.io/about/" class="nonmember" rel="external nofollow noopener" target="_blank">Anastasios Kyrillidis</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE ICRA 2024 Workshop—Back to the Future: Robot Learning Going Probabilistic</em> </div> <div class="periodical"> </div> <div class="links"> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://openreview.net/pdf?id=YEcJR7PTl8" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">Quintero2024wksp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Quintero-Peña, Carlos and Thomason, Wil and Kingston, Zachary and Kyrillidis, Anastasios and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE ICRA 2024 Workshop---Back to the Future: Robot Learning Going Probabilistic}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="na">url</span> <span class="p">=</span> <span class="s">{https://arxiv.org/pdf/2309.16862.pdf}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="Ramsey2024wksp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#505050"> <div> Workshop </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Dynamic Motion Planning from Perception via Accelerated Point Cloud Collision Checking</div> <div class="author"> <a href="https://claytonwramsey.com" class="nonmember" rel="external nofollow noopener" target="_blank">Clayton W. Ramsey</a>, <a href="/members/kingston">Zachary Kingston<sup>*</sup></a>, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason<sup>*</sup></a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE ICRA 2024 Workshop—Agile Robotics: From Perception to Dynamic Action</em> </div> <div class="periodical"> </div> <div class="links"> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">Ramsey2024wksp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Ramsey, Clayton W. and Kingston, Zachary and Thomason, Wil and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Dynamic Motion Planning from Perception via Accelerated Point Cloud Collision Checking}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE ICRA 2024 Workshop---Agile Robotics: From Perception to Dynamic Action}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="Meng2024wksp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#505050"> <div> Workshop </div> </abbr> </div> <div class="col-sm-10"> <div class="title">Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective</div> <div class="author"> <a href="https://www.linkedin.com/in/qingxi-meng-0b733a125/" class="nonmember" rel="external nofollow noopener" target="_blank">Qingxi Meng<sup>*</sup></a>, <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña<sup>*</sup></a>, <a href="/members/kingston">Zachary Kingston</a>, Nicole M. Fontenot, Shannan K. Hamlin, <a href="https://unhelkar.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Vaibhav Unhelkar</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE ICRA 2024 Workshop—Workshop on Nursing Robotics</em> </div> <div class="periodical"> </div> <div class="links"> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">Meng2024wksp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Meng, Qingxi and Quintero-Peña, Carlos and Kingston, Zachary and Fontenot, Nicole M. and Hamlin, Shannan K. and Unhelkar, Vaibhav and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Monitoring Constraints for Robotic Tutors in Nurse Education: A Motion Planning Perspective}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE ICRA 2024 Workshop---Workshop on Nursing Robotics}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2024}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2023</h2> <ol class="bibliography"> <li> <div class="row"> <a name="shome2023privacy" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA9200"> <a href="https://ieee-iros.org/" rel="external nofollow noopener" target="_blank"> IROS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Robots as AI Double Agents: Privacy in Motion Planning</div> <div class="author"> <a href="https://www.rahulsho.me/" class="nonmember" rel="external nofollow noopener" target="_blank">Rahul Shome</a>, <a href="/members/kingston">Zachary Kingston</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE/RSJ International Conference on Intelligent Robots and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/pdf/2308.03385.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/IROS55552.2023.10341460" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Robotics and automation are poised to change the landscape of home and work in the near future. Robots are adept at deliberately moving, sensing, and interacting with their environments. The pervasive use of this technology promises societal and economic payoffs due to its capabilities - conversely, the capabilities of robots to move within and sense the world around them is susceptible to abuse. Robots, unlike typical sensors, are inherently autonomous, active, and deliberate. Such automated agents can become AI double agents liable to violate the privacy of coworkers, privileged spaces, and other stakeholders. In this work we highlight the understudied and inevitable threats to privacy that can be posed by the autonomous, deliberate motions and sensing of robots. We frame the problem within broader sociotechnological questions alongside a comprehensive review. The privacy-aware motion planning problem is formulated in terms of cost functions that can be modified to induce privacy-aware behavior - preserving, agnostic, or violating. Simulated case studies in manipulation and navigation, with altered cost functions, are used to demonstrate how privacy-violating threats can be easily injected, sometimes with only small changes in performance (solution path lengths). Such functionality is already widely available. This preliminary work is meant to lay the foundations for near-future, holistic, interdisciplinary investigations that can address questions surrounding privacy in intelligent robotic behaviors determined by planning algorithms.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">shome2023privacy</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Shome, Rahul and Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Robots as AI Double Agents: Privacy in Motion Planning}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE/RSJ International Conference on Intelligent Robots and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{2861-2868}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/IROS55552.2023.10341460}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="bayraktar2023" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#BB921F"> <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank"> RA-L </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces</div> <div class="author"> S. Bora Bayraktar, <a href="https://www.aorthey.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Andreas Orthey</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://www.user.tu-berlin.de/mtoussai/" class="nonmember" rel="external nofollow noopener" target="_blank">Marc Toussaint</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>IEEE Robotics and Automation Letters</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://arxiv.org/pdf/2212.02955.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/LRA.2023.3282788" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through factors of simultaneously manipulatable joints of an object. Based on this factored state space, we propose less-actions RRT (LA-RRT), a planner which optimizes for a low number of actions to solve a puzzle. At the core of our approach lies a new path defragmentation method, which rearranges and optimizes consecutive edges to minimize action cost. We solve six rearrangement scenarios with a Fetch robot, involving planar table puzzles and an escape room scenario. LA-RRT significantly outperforms the next best asymptotically-optimal planner by 4.01 to 6.58 times improvement in final action cost.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">bayraktar2023</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Bayraktar, S. Bora and Orthey, Andreas and Kingston, Zachary and Toussaint, Marc and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{8}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{8}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{4529--4536}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/LRA.2023.3282788}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="lee2023physics" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Object Reconfiguration with Simulation-Derived Feasible Actions</div> <div class="author"> Yiyuan Lee, <a href="https://wbthomason.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Wil Thomason</a>, <a href="/members/kingston">Zachary Kingston</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://kavrakilab.org/publications/lee2023-simulation-actions.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/ICRA48891.2023.10160377" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired final configuration. Object reconfiguration is challenging to solve in general, as it requires efficient reasoning about environment physics that determine action validity. This information is typically manually encoded in an explicit transition system. Constructing these explicit encodings is tedious and error-prone, and is often a bottleneck for planner use. In this work, we explore embedding a physics simulator within a motion planner to implicitly discover and specify the valid actions from any state, removing the need for manual specification of action semantics. Our experiments demonstrate that the resulting simulation-based planner can effectively produce physically valid rearrangement trajectories for a range of 3D object reconfiguration problems without requiring more than an environment description and start and goal arrangements.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">lee2023physics</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Object Reconfiguration with Simulation-Derived Feasible Actions}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Lee, Yiyuan and Thomason, Wil and Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{8104-8111}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA48891.2023.10160377}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="quintero2023optimal" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Optimal Grasps and Placements for Task and Motion Planning in Clutter</div> <div class="author"> <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://www.tianyangpan.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Tianyang Pan</a>, <a href="https://www.rahulsho.me/" class="nonmember" rel="external nofollow noopener" target="_blank">Rahul Shome</a>, <a href="http://akyrillidis.github.io/about/" class="nonmember" rel="external nofollow noopener" target="_blank">Anastasios Kyrillidis</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://kavrakilab.org/publications/quintero2023-optimal-tmp.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/ICRA48891.2023.10161455" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Many methods that solve robot planning problems, such as task and motion planners, employ discrete symbolic search to find sequences of valid symbolic actions that are grounded with motion planning. Much of the efficacy of these planners lies in this grounding—bad placement and grasp choices can lead to inefficient planning when a problem has many geometric constraints. Moreover, grounding methods such as naı̈ve sampling often fail to find appropriate values for these choices in the presence of clutter. Towards efficient task and motion planning, we present a novel optimization-based approach for grounding to solve cluttered problems that have many constraints that arise from geometry. Our approach finds an optimal grounding and can provide feedback to discrete search for more effective planning. We demonstrate our method against baseline methods in complex simulated environments.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">quintero2023optimal</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Optimal Grasps and Placements for Task and Motion Planning in Clutter}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Quintero-Peña, Carlos and Kingston, Zachary and Pan, Tianyang and Shome, Rahul and Kyrillidis, Anastasios and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{3707-3713}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA48891.2023.10161455}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="kingston2023tro" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#9C793E"> <a href="https://www.ieee-ras.org/publications/t-ro" rel="external nofollow noopener" target="_blank"> T-RO </a> </abbr> <figure> <picture> <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/r2_walking-480.webp 480w,/assets/img/publication_preview/r2_walking-800.webp 800w,/assets/img/publication_preview/r2_walking-1400.webp 1400w," sizes="200px" type="image/webp"></source> <img src="/assets/img/publication_preview/r2_walking.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="r2_walking.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm-10"> <div class="title">Scaling Multimodal Planning: Using Experience and Informing Discrete Search</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a> and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>IEEE Transactions on Robotics</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2022-scaling-mmp.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/743110686?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://doi.org/10.1109/TRO.2022.3197080" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are multi-modal, such as pick-and-place tasks where every object grasp and placement is a mode. Multi-modal problems require finding a sequence of transitions between modes - for example, a particular sequence of object picks and placements. However, many multi-modal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multi-modal scalability in both these areas. For motion planning, we present an experience-based planning framework ALEF which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete lead to bias search towards useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multi-modal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kingston2023tro</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Transactions on Robotics}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Scaling Multimodal Planning: Using Experience and Informing Discrete Search}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2023}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{39}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{1}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{128--146}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/TRO.2022.3197080}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2022</h2> <ol class="bibliography"><li> <div class="row"> <a name="kingston2022robowflex" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA9200"> <a href="https://ieee-iros.org/" rel="external nofollow noopener" target="_blank"> IROS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Robowflex: Robot Motion Planning with MoveIt Made Easy</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a> and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE/RSJ International Conference on Intelligent Robots and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="award btn btn-sm z-depth-0" role="button">Nominated</a> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://kavrakilab.org/publications/kingston2022-robowflex.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/760062092?h=68a0b835cf&badge=0&autopause=0&player_id=0&app_id=58479" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://www.youtube.com/watch?v=mPDE3QSkLJ0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Talk</a> <a href="https://github.com/KavrakiLab/robowflex" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/IROS47612.2022.9981698" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="award hidden d-print-inline"> <p></p> <p>Nominated for Best Paper for Industrial Robotics Research for Practicality.</p> </div> <div class="abstract hidden"> <p>Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular MoveIt library and Robot Operating System (ROS) middleware. Robowflex takes advantage of the ease of motion planning with MoveIt while providing an augmented API to craft and manipulate motion planning queries within a single program. Robowflex’s high-level API simplifies many common use-cases while still providing access to the underlying MoveIt library. Robowflex is particularly useful for 1) developing new motion planners, 2) evaluation of motion planners, and 3) complex problems that use motion planning (e.g., task and motion planning). Robowflex also provides visualization capabilities, integrations to other robotics libraries (e.g., DART and Tesseract), and is complimentary to many other robotics packages. With our library, the user does not need to be an expert at ROS or MoveIt in order to set up motion planning queries, extract information from results, and directly interface with a variety of software components. We provide a few example use-cases that demonstrate its efficacy.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">kingston2022robowflex</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Robowflex: Robot Motion Planning with MoveIt Made Easy}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE/RSJ International Conference on Intelligent Robots and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2022}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{3108--3114}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/IROS47612.2022.9981698}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li></ol> <h2 class="bibliography">2021</h2> <ol class="bibliography"> <li> <div class="row"> <a name="chamzas2021mbm" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#BB921F"> <a href="https://www.ieee-ras.org/publications/ra-l" rel="external nofollow noopener" target="_blank"> RA-L </a> </abbr> </div> <div class="col-sm-10"> <div class="title">MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets</div> <div class="author"> <a href="https://cchamzas.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Constantinos Chamzas</a>, <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://www.aorthey.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Andreas Orthey</a>, <a href="https://dannyrakita.net/" class="nonmember" rel="external nofollow noopener" target="_blank">Daniel Rakita</a>, Michael Gleicher, <a href="https://www.user.tu-berlin.de/mtoussai/" class="nonmember" rel="external nofollow noopener" target="_blank">Marc Toussaint</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>IEEE Robotics and Automation Letters</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/chamzas2022-motion-bench-maker.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://www.youtube.com/watch?v=t96Py0QX0NI" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://www.youtube.com/watch?v=dCxXZWGQIlQ" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Talk</a> <a href="https://github.com/KavrakiLab/motion_bench_maker" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/LRA.2021.3133603" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Recently, there has been a wealth of development in motion planning for robotic manipulationnew motion planners are continuously proposed, each with its own unique set of strengths and weaknesses. However, evaluating these new planners is challenging, and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker, an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of over 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground for future motion planning research.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">chamzas2021mbm</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Chamzas, Constantinos and Quintero-Peña, Carlos and Kingston, Zachary and Orthey, Andreas and Rakita, Daniel and Gleicher, Michael and Toussaint, Marc and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{IEEE Robotics and Automation Letters}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{7}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{2}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{882--889}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/LRA.2021.3133603}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="kingston2021" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA9200"> <a href="https://ieee-iros.org/" rel="external nofollow noopener" target="_blank"> IROS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Using Experience to Improve Constrained Planning on Foliations for Multi-Modal Problems</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="https://cchamzas.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Constantinos Chamzas</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE/RSJ International Conference on Intelligent Robots and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://www.kavrakilab.org/publications/kingston2021experience-foliations.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/IROS51168.2021.9636236" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Many robotic manipulation problems are multi-modal—they consist of a discrete set of mode families (e.g., whether an object is grasped or placed) each with a continuum of parameters (e.g., where exactly an object is grasped). Core to these problems is solving single-mode motion plans, i.e., given a mode from a mode family (e.g., a specific grasp), find a feasible motion to transition to the next desired mode. Many planners for such problems have been proposed, but complex manipulation plans may require prohibitively long computation times due to the difficulty of solving these underlying single-mode problems. It has been shown that using experience from similar planning queries can significantly improve the efficiency of motion planning. However, even though modes from the same family are similar, they impose different constraints on the planning problem, and thus experience gained in one mode cannot be directly applied to another. We present a new experience-based framework, ALEF , for such multi-modal planning problems. ALEF learns using paths from single-mode problems from a mode family, and applies this experience to novel modes from the same family. We evaluate ALEF on a variety of challenging problems and show a significant improvement in the efficiency of sampling-based planners both in isolation and within a multi-modal manipulation planner.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">kingston2021</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Chamzas, Constantinos and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Using Experience to Improve Constrained Planning on Foliations for Multi-Modal Problems}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE/RSJ International Conference on Intelligent Robots and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{6922--6927}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/IROS51168.2021.9636236}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="moll2021hyper" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA9200"> <a href="https://ieee-iros.org/" rel="external nofollow noopener" target="_blank"> IROS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization</div> <div class="author"> <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, <a href="https://cchamzas.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Constantinos Chamzas</a>, <a href="/members/kingston">Zachary Kingston</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE/RSJ International Conference on Intelligent Robots and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/moll2021hyperplan.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://github.com/KavrakiLab/hyperplan" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/IROS51168.2021.9636651" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Over the years, many motion planning algorithms have been proposed. It is often unclear which algorithm might be best suited for a particular class of problems. The problem is compounded by the fact that algorithm performance can be highly dependent on parameter settings. This paper shows that hyperparameter optimization is an effective tool in both algorithm selection and parameter tuning over a given set of motion planning problems. We present different loss functions for optimization that capture different notions of optimality. The approach is evaluated on a broad range of scenes using two different manipulators, a Fetch and a Baxter. We show that optimized planning algorithm performance significantly improves upon baseline performance and generalizes broadly in the sense that performance improvements carry over to problems that are very different from the ones considered during optimization.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">moll2021hyper</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Moll, Mark and Chamzas, Constantinos and Kingston, Zachary and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{HyperPlan: A Framework for Motion Planning Algorithm Selection and Parameter Optimization}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE/RSJ International Conference on Intelligent Robots and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{2511-2518}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/IROS51168.2021.9636651}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="chamzas2021flame" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions</div> <div class="author"> <a href="https://cchamzas.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Constantinos Chamzas</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://carlosquinterop.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Carlos Quintero-Peña</a>, <a href="https://www.cs.rice.edu/~as143/" class="nonmember" rel="external nofollow noopener" target="_blank">Anshumali Shrivastava</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="award btn btn-sm z-depth-0" role="button">Nominated</a> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://www.kavrakilab.org/publications/chamzas2021-learn-sampling.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://www.youtube.com/watch?v=cH4_lIjjs58" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://github.com/KavrakiLab/pyre" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/ICRA48506.2021.9561104" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="award hidden d-print-inline"> <p></p> <p>Nominated for Best Paper in Cognitive Robotics.</p> </div> <div class="abstract hidden"> <p>Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with many degrees-of-freedom, these methods exhibit poor generalization across different environments and often require large datasets that are impractical to gather. We present SPARK and FLAME, two experience-based frameworks for sampling- based planning applicable to complex manipulators in 3D environments. Both combine samplers associated with features from a workspace decomposition into a global biased sampling distribution. SPARK decomposes the environment based on exact geometry while FLAME is more general, and uses an octree-based decomposition obtained from sensor data. We demonstrate the effectiveness of SPARK and FLAME on a real and simulated Fetch robot tasked with challenging pick-and-place manipulation problems. Our approaches can be trained incrementally and significantly improve performance with only a handful of examples, generalizing better over diverse tasks and environments as compared to prior approaches.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">chamzas2021flame</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Chamzas, Constantinos and Kingston, Zachary and Quintero-Peña, Carlos and Shrivastava, Anshumali and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA48506.2021.9561104}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1283--1289}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="wells2021icra" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Finite Horizon Synthesis for Probabilistic Manipulation Domains</div> <div class="author"> <a href="https://andrewmw94.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Andrew M. Wells</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://mortezalahijanian.com/" class="nonmember" rel="external nofollow noopener" target="_blank">Morteza Lahijanian</a>, <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a>, and <a href="https://www.cs.rice.edu/~vardi/" class="nonmember" rel="external nofollow noopener" target="_blank">Moshe Y. Vardi</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://www.kavrakilab.org/publications/wells2021-finite-horizon-synthesis.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1109/ICRA48506.2021.9561297" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Robots have begun operating and collaborating with humans in industrial and social settings. This collaboration introduces challenges: the robot must plan while taking the human’s actions into account. In prior work, the problem was posed as a 2-player deterministic game, with a limited number of human moves. The limit on human moves is unintuitive, and in many settings determinism is undesirable. In this paper, we present a novel planning method for collaborative human-robot manipulation tasks via probabilistic synthesis. We introduce a probabilistic manipulation domain that captures the interaction by allowing for both robot and human actions with states that represent the configurations of the objects in the workspace. The task is specified using Linear Temporal Logic over finite traces (LTLf). We then transform our manipulation domain into a Markov Decision Process (MDP) and synthesize an optimal policy to satisfy the specification on this MDP. We present two novel contributions: a formalization of probabilistic manipulation domains allowing us to apply existing techniques and a comparison of different encodings of these domains. Our framework is validated on a physical UR5 robot.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">wells2021icra</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Wells, Andrew M. and Kingston, Zachary and Lahijanian, Morteza and Kavraki, Lydia E. and Vardi, Moshe Y.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Finite Horizon Synthesis for Probabilistic Manipulation Domains}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2021}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA48506.2021.9561297}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{6336--6342}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2020</h2> <ol class="bibliography"> <li> <div class="row"> <a name="kingston2020book" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Planning Under Manifold Constraints, <i>Encyclopedia of Robotics</i> </div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a> </div> <div class="periodical"> </div> <div class="periodical"> </div> <div class="links"> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://doi.org/10.1007/978-3-642-41610-1_174-1" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inbook</span><span class="p">{</span><span class="nl">kingston2020book</span><span class="p">,</span> <span class="na">chapter</span> <span class="p">=</span> <span class="s">{Planning Under Manifold Constraints}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--9}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Encyclopedia of Robotics}</span><span class="p">,</span> <span class="na">publisher</span> <span class="p">=</span> <span class="s">{Springer Berlin Heidelberg}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary}</span><span class="p">,</span> <span class="na">editor</span> <span class="p">=</span> <span class="s">{Ang, Marcelo H. and Khatib, Oussama and Siciliano, Bruno}</span><span class="p">,</span> <span class="na">address</span> <span class="p">=</span> <span class="s">{Berlin, Heidelberg}</span><span class="p">,</span> <span class="na">isbn</span> <span class="p">=</span> <span class="s">{978-3-642-41610-1}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1007/978-3-642-41610-1_174-1}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="kingston2020leads" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Informing Multi-Modal Planning with Synergistic Discrete Leads</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="https://andrewmw94.github.io/" class="nonmember" rel="external nofollow noopener" target="_blank">Andrew M. Wells</a>, <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2020weighting-multi-modal-leads.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/393316293?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://doi.org/10.1109/ICRA40945.2020.9197545" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Robotic manipulation problems are inherently continuous, but typically have underlying discrete structure, e.g., whether or not an object is grasped. This means many problems are multi-modal and in particular have a continuous infinity of modes. For example, in a pick-and-place manipulation domain, every grasp and placement of an object is a mode. Usually manipulation problems require the robot to transition into different modes, e.g., going from a mode with an object placed to another mode with the object grasped. To successfully find a manipulation plan, a planner must find a sequence of valid single-mode motions as well as valid transitions between these modes. Many manipulation planners have been proposed to solve tasks with multi-modal structure. However, these methods require mode-specific planners and fail to scale to very cluttered environments or to tasks that require long sequences of transitions. This paper presents a general layered planning approach to multi-modal planning that uses a discrete "lead" to bias search towards useful mode transitions. The difficulty of achieving specific mode transitions is captured online and used to bias search towards more promising sequences of modes. We demonstrate our planner on complex scenes and show that significant performance improvements are tied to both our discrete "lead" and our continuous representation.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">kingston2020leads</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Wells, Andrew M. and Moll, Mark and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Informing Multi-Modal Planning with Synergistic Discrete Leads}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{3199--3205}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA40945.2020.9197545}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="kingston2020isrr" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#e33900"> <a href="http://ifrr.org/isrr" rel="external nofollow noopener" target="_blank"> ISRR </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Decoupling Constraints from Sampling-Based Planners</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In Robotics Research</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2017decoupling-constraints.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/261052837?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://ompl.kavrakilab.org/constrainedPlanning.html" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1007/978-3-030-28619-4_62" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>We present a general unifying framework for sampling-based motion planning under kinematic task constraints which enables a broad class of planners to compute plans that satisfy a given constraint function that encodes, e.g., loop closure, balance, and end-effector constraints. The framework decouples a planner’s method for exploration from constraint satisfaction by representing the implicit configuration space defined by a constraint function. We emulate three constraint satisfaction methodologies from the literature, and demonstrate the framework with a range of planners utilizing these constraint methodologies. Our results show that the appropriate choice of constrained satisfaction methodology depends on many factors, e.g., the dimension of the configuration space and implicit constraint manifold, and number of obstacles. Furthermore, we show that novel combinations of planners and constraint satisfaction methodologies can be more effective than previous approaches. The framework is also easily extended for novel planners and constraint spaces.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@incollection</span><span class="p">{</span><span class="nl">kingston2020isrr</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Moll, Mark and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Decoupling Constraints from Sampling-Based Planners}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Robotics Research}</span><span class="p">,</span> <span class="na">publisher</span> <span class="p">=</span> <span class="s">{Springer International Publishing}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2020}</span><span class="p">,</span> <span class="na">editor</span> <span class="p">=</span> <span class="s">{Amato, N. M. and Hager, G. and Thomas, S. and Torres-Torriti, M.}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{913--928}</span><span class="p">,</span> <span class="na">address</span> <span class="p">=</span> <span class="s">{Cham}</span><span class="p">,</span> <span class="na">isbn</span> <span class="p">=</span> <span class="s">{978-3-030-28619-4}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1007/978-3-030-28619-4_62}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2019</h2> <ol class="bibliography"><li> <div class="row"> <a name="kingston2019imacs" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#7D615D"> <a href="https://journals.sagepub.com/home/ijr" rel="external nofollow noopener" target="_blank"> IJRR </a> </abbr> <figure> <picture> <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/parallel-480.webp 480w,/assets/img/publication_preview/parallel-800.webp 800w,/assets/img/publication_preview/parallel-1400.webp 1400w," sizes="200px" type="image/webp"></source> <img src="/assets/img/publication_preview/parallel.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="parallel.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm-10"> <div class="title">Exploring Implicit Spaces for Constrained Sampling-Based Planning</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>The International Journal of Robotics Research</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2019exploring-implicit-spaces-for-constrained.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://ompl.kavrakilab.org/constrainedPlanning.html" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1177/0278364919868530" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>We present a review and reformulation of manifold constrained sampling-based motion planning within a unifying framework, IMACS (implicit manifold configuration space). IMACS enables a broad class of motion planners to plan in the presence of manifold constraints, decoupling the choice of motion planning algorithm and method for constraint adherence into orthogonal choices. We show that implicit configuration spaces defined by constraints can be presented to sampling-based planners by addressing two key fundamental primitives, sampling and local planning, and that IMACS preserves theoretical properties of probabilistic completeness and asymptotic optimality through these primitives. Within IMACS, we implement projection- and continuation-based methods for constraint adherence, and demonstrate the framework on a range of planners with both methods in simulated and realistic scenarios. Our results show that the choice of method for constraint adherence depends on many factors and that novel combinations of planners and methods of constraint adherence can be more effective than previous approaches. Our implementation of IMACS is open source within the Open Motion Planning Library and is easily extended for novel planners and constraint spaces.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kingston2019imacs</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Moll, Mark and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Exploring Implicit Spaces for Constrained Sampling-Based Planning}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{The International Journal of Robotics Research}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2019}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{38}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{10--11}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1151--1178}</span><span class="p">,</span> <span class="na">month</span> <span class="p">=</span> <span class="nv">sep</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1177/0278364919868530}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li></ol> <h2 class="bibliography">2018</h2> <ol class="bibliography"> <li> <div class="row"> <a name="habibi2018dars" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Distributed Object Characterization with Local Sensing by a Multi-Robot System</div> <div class="author"> <a href="https://www.ou.edu/coe/cs/people/faculty/golnaz-habibi" class="nonmember" rel="external nofollow noopener" target="_blank">Golnaz Habibi</a>, Sándor P. Fekete, <a href="/members/kingston">Zachary Kingston</a>, and James McLurkin </div> <div class="periodical"> <em>In Distributed Autonomous Robotic Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://s3.amazonaws.com/zk-bucket/rsc/Habibi2018.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/287250201?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://doi.org/10.1007/978-3-319-73008-0_15" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>This paper presents two distributed algorithms for enabling a swarm of robots with local sensing and local coordinates to estimate the dimensions and orientation of an unknown complex polygonal object, ie, its minimum and maximum width and its main axis. Our first approach is based on a robust heuristic of distributed Principal Component Analysis (DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The result show our algorithms successfully estimate the dimension and orientation of convex or concave objects with a reasonable error in the presence of noisy data.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@incollection</span><span class="p">{</span><span class="nl">habibi2018dars</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Habibi, Golnaz and Fekete, S{\'a}ndor P. and Kingston, Zachary and McLurkin, James}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Distributed Object Characterization with Local Sensing by a Multi-Robot System}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Distributed Autonomous Robotic Systems}</span><span class="p">,</span> <span class="na">publisher</span> <span class="p">=</span> <span class="s">{Springer Proceedings in Advanced Robotics}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span class="p">,</span> <span class="na">editor</span> <span class="p">=</span> <span class="s">{Gro{\ss}, Roderich and Kolling, Andreas and Berman, Spring and Frazzoli, Emilio and Martinoli, Alcherio and Matsuno, Fumitoshi and Gauci, Melvin}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{6}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{205--218}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1007/978-3-319-73008-0_15}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="dantam2018tmp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#7D615D"> <a href="https://journals.sagepub.com/home/ijr" rel="external nofollow noopener" target="_blank"> IJRR </a> </abbr> </div> <div class="col-sm-10"> <div class="title">An Incremental Constraint-Based Framework for Task and Motion Planning</div> <div class="author"> <a href="http://www.neil.dantam.name/" class="nonmember" rel="external nofollow noopener" target="_blank">Neil T. Dantam</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://www.cs.utexas.edu/~swarat/" class="nonmember" rel="external nofollow noopener" target="_blank">Swarat Chaudhuri</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>The International Journal of Robotics Research, Special Issue on the 2016 Robotics: Science and Systems Conference</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/dantam2018incremental-tmp.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="http://tmkit.kavrakilab.org/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1177/0278364918761570" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstrations necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-the-art, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">dantam2018tmp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Dantam, Neil T. and Kingston, Zachary and Chaudhuri, Swarat and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{An Incremental Constraint-Based Framework for Task and Motion Planning}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{The International Journal of Robotics Research, Special Issue on the 2016 Robotics: Science and Systems Conference}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{37}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{10}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1134--1151}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1177/0278364918761570}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="kingston2018ar" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#555960"> <a href="https://www.annualreviews.org/" rel="external nofollow noopener" target="_blank"> Ann. Rev. </a> </abbr> <figure> <picture> <source class="responsive-img-srcset" srcset="/assets/img/publication_preview/r2_tasks-480.webp 480w,/assets/img/publication_preview/r2_tasks-800.webp 800w,/assets/img/publication_preview/r2_tasks-1400.webp 1400w," sizes="200px" type="image/webp"></source> <img src="/assets/img/publication_preview/r2_tasks.jpg" class="preview z-depth-1 rounded" width="100%" height="auto" alt="r2_tasks.jpg" data-zoomable="" loading="eager" onerror="this.onerror=null; $('.responsive-img-srcset').remove();"> </picture> </figure> </div> <div class="col-sm-10"> <div class="title">Sampling-Based Methods for Motion Planning with Constraints</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>Annual Review of Control, Robotics, and Autonomous Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2018sampling-based-methods-for-motion-planning.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.1146/annurev-control-060117-105226" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, incorporating task constraints (e.g., keeping a cup level, writing on a board) into the planning process introduces significant challenges. is survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based upon two core primitive operations: (1) sampling constraint-satisfying configurations and (2) generating constraint-satisfying continuous motion. Although the basics of sampling-based planning are presented for contextual background, the survey focuses on the representation of constraints and sampling-based planners that incorporate constraints.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@article</span><span class="p">{</span><span class="nl">kingston2018ar</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Moll, Mark and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Sampling-Based Methods for Motion Planning with Constraints}</span><span class="p">,</span> <span class="na">journal</span> <span class="p">=</span> <span class="s">{Annual Review of Control, Robotics, and Autonomous Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2018}</span><span class="p">,</span> <span class="na">volume</span> <span class="p">=</span> <span class="s">{1}</span><span class="p">,</span> <span class="na">number</span> <span class="p">=</span> <span class="s">{1}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{159--185}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1146/annurev-control-060117-105226}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> <h2 class="bibliography">2017</h2> <ol class="bibliography"><li> <div class="row"> <a name="baker2017r2" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Robonaut 2 and You: Specifying and Executing Complex Operations</div> <div class="author"> William Baker, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://moll.ai/" class="nonmember" rel="external nofollow noopener" target="_blank">Mark Moll</a>, Julia Badger, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE Workshop on Advanced Robotics and its Social Impacts</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/baker2017robonaut-2-and-you.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/287250170?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://doi.org/10.1109/ARSO.2017.8025204" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">baker2017r2</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Baker, William and Kingston, Zachary and Moll, Mark and Badger, Julia and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Robonaut 2 and You: Specifying and Executing Complex Operations}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE Workshop on Advanced Robotics and its Social Impacts}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2017}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1--8}</span><span class="p">,</span> <span class="na">address</span> <span class="p">=</span> <span class="s">{Austin, TX}</span><span class="p">,</span> <span class="na">month</span> <span class="p">=</span> <span class="nv">mar</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ARSO.2017.8025204}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li></ol> <h2 class="bibliography">2016</h2> <ol class="bibliography"><li> <div class="row"> <a name="dantam2016tmp" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#DA7900"> <a href="https://roboticsconference.org/" rel="external nofollow noopener" target="_blank"> RSS </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Incremental Task and Motion Planning: A Constraint-Based Approach</div> <div class="author"> <a href="http://www.neil.dantam.name/" class="nonmember" rel="external nofollow noopener" target="_blank">Neil T. Dantam</a>, <a href="/members/kingston">Zachary Kingston</a>, <a href="https://www.cs.utexas.edu/~swarat/" class="nonmember" rel="external nofollow noopener" target="_blank">Swarat Chaudhuri</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In Robotics: Science and Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/dantam2016tmp.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://youtu.be/QHCuD0tOdfY?si=dCfjMJHFuLzxMNom" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://youtu.be/9EcOJ8mF5JE?si=9uhdZpqSHZh25oIS" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Talk</a> <a href="http://tmkit.kavrakilab.org/" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.15607/RSS.2016.XII.002" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstractions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-the-art, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">dantam2016tmp</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Dantam, Neil T. and Kingston, Zachary and Chaudhuri, Swarat and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Incremental Task and Motion Planning: A Constraint-Based Approach}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Robotics: Science and Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2016}</span><span class="p">,</span> <span class="na">address</span> <span class="p">=</span> <span class="s">{Ann Arbor, MI}</span><span class="p">,</span> <span class="na">month</span> <span class="p">=</span> <span class="nv">jun</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.15607/RSS.2016.XII.002}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li></ol> <h2 class="bibliography">2015</h2> <ol class="bibliography"> <li> <div class="row"> <a name="kingston2015lc3" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Kinematically Constrained Workspace Control via Linear Optimization</div> <div class="author"> <a href="/members/kingston">Zachary Kingston</a>, <a href="http://www.neil.dantam.name/" class="nonmember" rel="external nofollow noopener" target="_blank">Neil T. Dantam</a>, and <a href="https://profiles.rice.edu/faculty/lydia-e-kavraki" class="nonmember" rel="external nofollow noopener" target="_blank">Lydia E. Kavraki</a> </div> <div class="periodical"> <em>In IEEE-RAS International Conference on Humanoid Robots</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="http://kavrakilab.org/publications/kingston2015lc3.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://youtu.be/Jl6AmQLjT8w?si=h7PrdHmonGGxw_We" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="http://amino.dyalab.org" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="https://doi.org/10.1109/HUMANOIDS.2015.7363455" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>We present a method for Cartesian workspace control of a robot manipulator that enforces joint-level acceleration, velocity, and position constraints using linear optimization. This method is robust to kinematic singularities. On redundant manipulators, we avoid poor configurations near joint limits by including a maximum permissible velocity term to center each joint within its limits. Compared to the baseline Jacobian damped least-squares method of workspace control, this new approach honors kinematic limits, ensuring physically realizable control inputs and providing smoother motion of the robot. We demonstrate our method on simulated redundant and non-redundant manipulators and implement it on the physical 7-degree-of-freedom Baxter manipulator. We provide our control software under a permissive license.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">kingston2015lc3</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Kingston, Zachary and Dantam, Neil T. and Kavraki, Lydia E.}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Kinematically Constrained Workspace Control via Linear Optimization}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE-RAS International Conference on Humanoid Robots}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2015}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{758--764}</span><span class="p">,</span> <span class="na">month</span> <span class="p">=</span> <span class="nv">nov</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/HUMANOIDS.2015.7363455}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="habibi2015aamas" class="anchor"></a> <div class="col col-sm-2 abbr"> </div> <div class="col-sm-10"> <div class="title">Pipelined Consensus for Global State Estimation in Multi-Agent Systems</div> <div class="author"> <a href="https://www.ou.edu/coe/cs/people/faculty/golnaz-habibi" class="nonmember" rel="external nofollow noopener" target="_blank">Golnaz Habibi</a>, <a href="/members/kingston">Zachary Kingston</a>, Zijian Wang, Mac Schwager, and James McLurkin </div> <div class="periodical"> <em>In Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://zkingston.com/papers/habibi2015aamas.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://doi.org/10.5555/2772879.2773320" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>This paper presents pipelined consensus, an extension of pair-wise gossip-based consensus, for multi-agent systems using mesh networks. Each agent starts a new consensus in each round of gossiping, and stores the intermediate results for the previous k consensus in a pipeline message. After k rounds of gossiping, the results of the first consensus are ready. The pipeline keeps each consensus independent, so any errors only persist for k rounds. This makes pipelined consensus robust to many real-world problems that other algorithms cannot handle, including message loss, changes in network topology, sensor variance, and changes in agent population. The algorithm is fully distributed and self-stabilizing, and uses a communication message of fixed size. We demonstrate the efficiency of pipelined consensus in two scenarios: computing mean sensor values in a distributed sensor network, and computing a centroid estimate in a multi-robot system. We provide extensive simulation results, and real-world experiments with up to 24 agents. The algorithm produces accurate results, and handles all of the disturbances mentioned above.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@incollection</span><span class="p">{</span><span class="nl">habibi2015aamas</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Habibi, Golnaz and Kingston, Zachary and Wang, Zijian and Schwager, Mac and McLurkin, James}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Pipelined Consensus for Global State Estimation in Multi-Agent Systems}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems}</span><span class="p">,</span> <span class="na">publisher</span> <span class="p">=</span> <span class="s">{International Foundation for Autonomous Agents and Multiagent Systems}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2015}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1315--1323}</span><span class="p">,</span> <span class="na">isbn</span> <span class="p">=</span> <span class="s">{9781450334136}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.5555/2772879.2773320}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> <li> <div class="row"> <a name="habibi2015icra" class="anchor"></a> <div class="col col-sm-2 abbr"> <abbr class="badge rounded w-100" style="background-color:#daaa00"> <a href="https://ieee-icra.org" rel="external nofollow noopener" target="_blank"> ICRA </a> </abbr> </div> <div class="col-sm-10"> <div class="title">Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems</div> <div class="author"> <a href="https://www.ou.edu/coe/cs/people/faculty/golnaz-habibi" class="nonmember" rel="external nofollow noopener" target="_blank">Golnaz Habibi</a>, <a href="/members/kingston">Zachary Kingston</a>, William Xie, Mathew Jellins, and James McLurkin </div> <div class="periodical"> <em>In IEEE International Conference on Robotics and Automation</em> </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a class="bibtex btn btn-sm z-depth-0" role="button">Bib</a> <a href="https://zkingston.com/papers/habibi2015icra.pdf" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">PDF</a> <a href="https://player.vimeo.com/video/287250199?loop=1&color=ffffff&byline=0&portrait=0" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://doi.org/10.1109/ICRA.2015.7139356" rel="external nofollow noopener" target="_blank"> <i class="ai ai-doi ml-1"> </i> </a> </div> <div class="abstract hidden"> <p>This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.</p> </div> <div class="bibtex hidden"> <figure class="highlight"><pre><code class="language-bibtex" data-lang="bibtex"><span class="nc">@inproceedings</span><span class="p">{</span><span class="nl">habibi2015icra</span><span class="p">,</span> <span class="na">author</span> <span class="p">=</span> <span class="s">{Habibi, Golnaz and Kingston, Zachary and Xie, William and Jellins, Mathew and McLurkin, James}</span><span class="p">,</span> <span class="na">title</span> <span class="p">=</span> <span class="s">{Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems}</span><span class="p">,</span> <span class="na">booktitle</span> <span class="p">=</span> <span class="s">{IEEE International Conference on Robotics and Automation}</span><span class="p">,</span> <span class="na">year</span> <span class="p">=</span> <span class="s">{2015}</span><span class="p">,</span> <span class="na">pages</span> <span class="p">=</span> <span class="s">{1282--1288}</span><span class="p">,</span> <span class="na">doi</span> <span class="p">=</span> <span class="s">{10.1109/ICRA.2015.7139356}</span><span class="p">,</span> <span class="p">}</span></code></pre></figure> </div> </div> </div> </li> </ol> </div> </article> </div> </div> <footer class="sticky-bottom mt-5" role="contentinfo"> <div class="container"> CoMMA Lab @ <a href="https://www.purdue.edu/" rel="external nofollow noopener" target="_blank">Purdue University</a>, <a href="https://www.cs.purdue.edu/" rel="external nofollow noopener" target="_blank">Department of Computer Science</a>. © Copyright 2025. 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