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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>A²R Lab</title>
<link>https://a2r-lab.github.io/</link>
<description>Recent content on A²R Lab</description>
<generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator>
<language>en-us</language>
<copyright>&copy; {year} Brian Plancher</copyright>
<lastBuildDate>Mon, 09 Nov 2026 00:00:03 +0000</lastBuildDate>
<atom:link href="https://a2r-lab.github.io/index.xml" rel="self" type="application/rss+xml" />
<item>
<title>Publications</title>
<link>https://a2r-lab.github.io/publication/</link>
<pubDate>Mon, 09 Nov 2026 00:00:03 +0000</pubDate>
<guid>https://a2r-lab.github.io/publication/</guid>
<description><p>This list includes publications from the PI&rsquo;s time at the Harvard <a href="https://agile.seas.harvard.edu/">Agile Robotics</a> and <a href="https://edge.seas.harvard.edu/">Edge Computing</a> Labs, as well as when the A2R Lab was affiliated with the <a href="https://cs.barnard.edu/">Department of Computer Science at Barnard College, Columbia University</a>.</p>
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<item>
<title>Research Projects</title>
<link>https://a2r-lab.github.io/projects/</link>
<pubDate>Sat, 21 Aug 2021 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/projects/</guid>
<description><p>Our lab&rsquo;s core research question is: <em>how can we construct computational systems that enable robots to intelligently, flexibility, and reliably operate in the field?</em> We seek to address this problem by <strong>developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, at all scales, through algorithm-hardware-software co-design</strong>. This approach requires designing theoretically sound optimization- and learning-based algorithms (e.g., model predictive control) that run at order-of-magnitude faster rates on edge computational hardware ranging from small-scale MCUs, to large-scale GPUs and FPGAs, and even to custom ASICs and non von Neumann architectures (e.g., neuromorphic processors). As such, we work across the computational stack, designing algorithms, software systems, and computational hardware at the intersection of robotics, optimization, computer architecture / systems, and machine learning. At the same time we work to promote a responsible, sustainable, and accessible future for robotics and edge computing, including the development of new interdisciplinary, project-based, open-access courses that lower the barriers to entry for cutting-edge topics like robotics, parallel programming, and embedded machine learning.</p>
</description>
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<title></title>
<link>https://a2r-lab.github.io/people/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/people/</guid>
<description></description>
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<item>
<title>A²R Lab at ICRA 2024</title>
<link>https://a2r-lab.github.io/icra-24/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/icra-24/</guid>
<description><h3 id="paper-presentations-and-posters">Paper Presentations and Posters</h3>
<p><strong>Tuesday 10:30am-12:00pm</strong> &ndash; Poster Session Tuesday 1:30pm-3:00pm</p>
<ul>
<li><a href="https://a2r-lab.org/publication/tinympc/"><strong>TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers</strong></a> (TuAA1-CC.1 - Award Session - CC-Main Hall)</li>
</ul>
<!-- raw HTML omitted -->
<p><strong>Tuesday 1:30pm-3:00pm</strong> &ndash; Poster Session Tuesday 4:30pm-6:00pm</p>
<ul>
<li><a href="https://a2r-lab.org/publication/diffcompressdrl/"><strong>Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning</strong></a> (TuBT8-CC.7 - Reinforcement Learning I - CC-418)</li>
</ul>
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<p><strong>Wednesday 10:30am-12:00pm</strong></p>
<ul>
<li><a href="https://a2r-lab.org/publication/robotperf/"><strong>RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance</strong></a> (WeAT20-NT.8 - Performance Evaluation and Benchmarking - NT-G302)</li>
</ul>
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<p><strong>Wednesday 1:30pm-3:00pm</strong> &ndash; Poster Session Wednesday 4:30pm-6:00pm</p>
<ul>
<li><a href="https://a2r-lab.org/publication/symstair/"><strong>Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization</strong></a> (WeBT18-AX.3 - Optimization and Optimal Control I - AX-206)</li>
</ul>
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<ul>
<li><a href="https://a2r-lab.org/publication/mpcgpu/"><strong>MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU</strong></a> (WeBT18-AX.4 - Optimization and Optimal Control I - AX-206)</li>
</ul>
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<h3 id="workshop-panels-and-presentations">Workshop Panels and Presentations</h3>
<p><strong>Friday 4:30-6:00pm</strong></p>
<ul>
<li><a href="https://sites.google.com/site/adrienescandehomepage/ICRA2024ClimateChange"><strong>Workshop on Robots and Roboticists in the Age of Climate Change</strong> &ndash; Panel Discussion</a></li>
</ul>
</description>
</item>
<item>
<title>A²R Lab at ICRA 2026</title>
<link>https://a2r-lab.github.io/icra-26/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/icra-26/</guid>
<description><p><img alt="A2R Lab @ ICRA-26" src="https://a2r-lab.github.io/icra-26/cover.png"></p>
<h3 id="icra-and-ra-l-papers">ICRA and RA-L Papers</h3>
<p><strong>Tuesday 9:00am-10:30am</strong> &ndash; Poster Session</p>
<ul>
<li><a href="https://a2r-lab.org/publication/conictinympc/"><strong>Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC</strong></a> by Ishaan Mahajan, Khai Nguyen, Sam Schoedel, Elakhya Nedumaran, Moises Mata, Brian Plancher and Zachary Manchester (TuI1I.186)</li>
<li><a href="https://a2r-lab.org/publication/roboprec/"><strong>RoboPrec: Enabling Reliable Embedded Computing for Robotics by Providing Accuracy Guarantees across Mixed-Precision Datatypes</strong></a> by Alp Eren Yilmaz, Lillian Pentecost, Thomas Bourgeat, Brian Plancher and Sabrina M. Neuman (TuI1I.377)</li>
</ul>
<p><strong>Tuesday 3:00pm-4:30pm</strong> &ndash; Poster Session</p>
<ul>
<li><a href="https://commalab.org/papers/pRRTC/"><strong>pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning</strong></a> by Chih Huang, Pranav Jadhav, Brian Plancher and Zachary Kingston (TuI2I.243)</li>
</ul>
<p><strong>Wednesday 9:00am-10:30am</strong> &ndash; Poster Session</p>
<ul>
<li><a href="https://a2r-lab.org/TAG-K/"><strong>TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation</strong></a> by Shuo &ldquo;Chris&rdquo; Sha, Anupam Bhakta, Justin Jiang, Kevin Qiu, Ishaan Mahajan, Gabriel Bravo Palacios and Brian Plancher (WeI1I.307)</li>
</ul>
<p><strong>Thursday 3:00pm-4:30pm</strong> &ndash; Poster Session</p>
<ul>
<li><a href="https://a2r-lab.org/GATO/"><strong>GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control</strong></a> by Alexander Du, Emre Adabag, Gabriel Bravo-Palacios and Brian Plancher (ThI2I.218)</li>
</ul>
<h3 id="workshop-papers">Workshop Papers</h3>
<p><strong>Monday - <a href="https://sites.google.com/robotics.utias.utoronto.ca/icra26-frontiers-optimization/home">Frontiers of Optimization for Robotics</a></strong></p>
<ul>
<li><strong>Differentiable and Constrained Model Predictive Control on the GPU</strong> by Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov, Brian Plancher, Thomas Lew &ndash; <em>early results from our recently announced <a href="https://home.dartmouth.edu/news/2026/05/brian-plancher-wins-toyota-research-institute-grant?utm_source=linkedin_link&utm_medium=social&utm_campaign=sciences">TRI University 3.0 Project</a></em></li>
<li><a href="https://commalab.org/papers/pRRTC/"><strong>pAORRTC: GPU-Parallel Almost-Surely Asymptotically Optimal Planning</strong></a> by Chih Huang, Zachary Kingston and Brian Plancher &ndash; <em>an extension to pRRTC</em></li>
<li><strong>End-to-End Hardware-Algorithm Co-Design for High-Rate FPGA-Accelerated MPC on Tiny Drones</strong> by Andrea Grillo and Brian Plancher</li>
</ul>
<p><strong>Friday - <a href="https://sites.google.com/view/roboarch-icra26">RoboARCH: Robotics Acceleration with Computing Hardware and Systems</a></strong></p>
<ul>
<li><strong>Differentiable and Constrained Model Predictive Control on the GPU</strong> by Gabriel Bravo-Palacios, Jianghan Zhang, Zachary Pestrikov, Brian Plancher, Thomas Lew &ndash; <em>early results from our recently announced <a href="https://home.dartmouth.edu/news/2026/05/brian-plancher-wins-toyota-research-institute-grant?utm_source=linkedin_link&utm_medium=social&utm_campaign=sciences">TRI University 3.0 Project</a></em></li>
<li><a href="https://a2r-lab.org/TinySDP/"><strong>TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics</strong></a> by Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo, Fausto Vega, James Anderson, Zachary Manchester and Brian Plancher &ndash; <em>accepted to RSS 2026</em></li>
<li><a href="https://commalab.org/papers/pRRTC/"><strong>pAORRTC: GPU-Parallel Almost-Surely Asymptotically Optimal Planning</strong></a> by Chih Huang, Zachary Kingston and Brian Plancher &ndash; <em>an extension to pRRTC</em></li>
<li><strong>Provably Accurate Fixed-Point Arithmetic for Robotics Accelerators</strong> by Alp Eren Yilmaz, Lillian Pentecost, Thomas Bourgeat, Brian Plancher and Sabrina M. Neuman &ndash; <em>an extension to RoboPrec</em></li>
<li><strong>End-to-End Hardware-Algorithm Co-Design for High-Rate FPGA-Accelerated MPC on Tiny Drones</strong> by Andrea Grillo and Brian Plancher</li>
<li><strong>Redacted for Double-Blind Review</strong> by Tanmay Desai, Ruth Iris Bahar and Brian Plancher</li>
</ul>
</description>
</item>
<item>
<title>Join Us</title>
<link>https://a2r-lab.github.io/join/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/join/</guid>
<description><ul>
<li><strong>We are recruiting for PhD researchers for Fall 2027 starts</strong>. Please apply through the <a href="https://graduate.dartmouth.edu/academics/programs/computer-science">Dartmouth CS PhD program</a> at the Guarini School of Graduate and Advanced Studies by <strong>December 15th</strong>.
<ul>
<li><strong>All hiring will center on the lab&rsquo;s core focus of algorithm-hardware-software co-design for robotics. We are particularly excited about expanding our work with emerging compute platforms (e.g., FPGAs, ASICs, neuromorphic, and analog computing), alongside our ongoing work on GPUs, embedded systems, numerical optimization, and robot learning.</strong></li>
<li>In your application, please select Robotics as an area of interest and list Brian Plancher as one of the faculty members you are particularly interested in working with.</li>
<li><strong>Dartmouth offers <a href="https://graduate.dartmouth.edu/admissions/applying-dartmouth/fee-waiver-criteria">application fee waivers</a></strong> to applicants meeting a number of eligibility criteria.</li>
<li>Due to the volume of prospective-student inquiries, we are generally unable to meet individually with applicants or pre-review application materials before submission. We will carefully review applications during the admissions process and reach out directly to candidates whose interests and background appear to be a particularly strong match.</li>
</ul>
</li>
<li><strong>Dartmouth Undergraduates and Master&rsquo;s Student</strong> research opportunities vary substantially by term, project needs, mentoring capacity, and available funding. The best way to prepare to work with the lab is generally to take <strong><a href="https://brianplancher.com/courses/169.23-f25">Parallel Optimization for Robotics</a></strong> which will next be taught in the Winter 2027 Term. See the information below for additional details.</li>
<li><strong>We do not currently have any open postdoctoral positions</strong>. Any future funded postdoctoral openings will be posted on this page.</li>
<li><strong>We generally do not take on remote or external research assistants who have not previously worked with the lab in person.</strong> Robotics research frequently requires time in the lab to deploy and evaluate systems on physical robots, and our experience has been that research collaborations are substantially more successful after first working together in person. In exceptional cases, we may consider remote work with someone who has previously worked with the lab in person, typically through a course or term-time independent study. If you are not currently at Dartmouth and have not previously worked with the lab, you should generally not expect us to be able to offer a research project.</li>
</ul>
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<hr>
<p>We welcome students with a wide range of technical backgrounds who are interested in our research. Our work is highly interdisciplinary, spanning robotics, computer architecture, embedded systems, numerical optimization, and machine learning, with a unifying focus on developing <strong>edge computational systems through algorithm-hardware-software co-design</strong>.</p>
<p>Because of this breadth, we do not expect incoming researchers to already have experience in every area. Prior experience in robotics, parallel programming, or machine learning is helpful but not required. Instead, we particularly value strong foundations in <strong>applied mathematics, computer systems, and software engineering</strong>, as these skills translate across many of our projects and provide a strong foundation for learning project-specific material.</p>
<p>Most importantly, we value <strong>intellectual curiosity</strong>, <strong>commitment</strong>, <strong>clear communication</strong>, <strong>creativity</strong>, and the <strong>courage to learn something new</strong>. Students who enjoy bridging multiple technical domains and engaging deeply in collaborative research are likely to be a good fit. We also care about <strong>real-world impact</strong> and encourage contributions to <strong>outreach and education</strong>.</p>
<p>For Dartmouth undergraduates, master&rsquo;s students, and PhD researchers interested in the lab, we strongly recommend taking <strong><a href="https://brianplancher.com/courses/169.23-f25">Parallel Optimization for Robotics</a></strong>. It provides preparation for much of our research, and its course project can be an excellent way to begin exploring related research directions.</p>
<h3 id="expectations-for-part-time-undergraduate-and-masters-researchers">Expectations for Part-Time Undergraduate and Master&rsquo;s Researchers</h3>
<p>To make sure that term-time research can be a meaningful experience, we generally expect undergraduate and master&rsquo;s researchers to:</p>
<ul>
<li>Commit at least <strong>10 hours per week</strong> to research, roughly comparable to the time commitment of a course. Independent-study research credit may be possible for Dartmouth students.</li>
<li>Provide a <strong>written progress update</strong> each week and discuss it through a scheduled project meeting.</li>
<li>Participate <strong>in person</strong> (where applicable) for project meetings, hardware deployments, etc.</li>
</ul>
<p>Research capacity varies from term to term, and meeting these expectations does not by itself guarantee that we will have an appropriate project or available mentoring capacity.</p>
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<h3 id="faqs">FAQs:</h3>
<p><em><strong>What kinds of research is the lab doing right now?</strong></em>
You can find descriptions of current research directions on our <a href="https://a2r-lab.github.io/projects/">projects page</a> and our recent work on our <a href="https://a2r-lab.github.io/publication/">publications page</a>.</p>
<p>Broadly, our research focuses on <strong>performance engineering for computational robotics at the edge through algorithm-hardware-software co-design</strong>. Current and future directions include accelerated numerical optimization and model predictive control on GPUs; efficient robotics algorithms for resource-constrained platforms such as microcontrollers; hybrid approaches combining learning, sampling, and optimization; and emerging robotics computing architectures including FPGAs, ASICs, and neuromorphic systems.</p>
<p>Because individual projects often require substantial depth in a particular technical area, there is no single set of prerequisites for every project.</p>
<p><em><strong>Is research funded?</strong></em></p>
<p><strong>Funding for undergraduate and master&rsquo;s research should not be assumed and is not guaranteed.</strong> Available funding varies substantially by project, student eligibility, grant support, and time of year. Research may also be possible through an <strong>independent study for academic credit</strong>.</p>
<p>Dartmouth students are strongly encouraged to explore <a href="https://students.dartmouth.edu/surfd/research/getting-started/dartmouth-research-funding">established Dartmouth research programs and other College funding opportunities</a>.
From time to time, the lab may have grant-supported paid research positions, but availability is limited and varies from term to term. We cannot guarantee that joining the lab will lead to funding now or in the future.</p>
<p><strong>Admitted PhD students have 5 years of guaranteed funding through the Dartmouth Computer Science PhD program</strong> rather than through the undergraduate/master&rsquo;s research process described above.</p>
<p><em><strong>Can I work with the lab remotely?</strong></em></p>
<p>As a general rule, <strong>we do not support purely remote research assistants</strong>. Our research frequently involves physical robotic systems, and even projects that are primarily computational often eventually require in-lab deployment and evaluation. In addition, our experience has been that remote collaborations work much better after first establishing a successful in-person working relationship. We therefore typically require researchers to work with us in person first (e.g., through a course or term-time independent study) before we would consider a subsequent remote arrangement.</p>
<p><em><strong>Should I email before applying to the PhD program? Can you review my application?</strong></em></p>
<p>You are welcome to indicate your interest in the A²R Lab in your application, and you should list Brian Plancher among the faculty you are particularly interested in working with.
However, due to the volume of prospective-student inquiries, Brian is generally unable to schedule meetings with prospective applicants or provide application pre-reviews before the admissions deadline. You do not need to contact the lab in advance to receive full consideration. Applications will be reviewed carefully as part of the normal Dartmouth CS admissions process, and we will reach out directly to candidates whose interests appear to be a strong match.</p>
<p><em><strong>Where can I learn more or prepare for research in the lab?</strong></em></p>
<p>For Dartmouth students, <strong><a href="https://brianplancher.com/courses/169.23-f25">Parallel Optimization for Robotics</a></strong> is excellent preparation for many of our projects.</p>
<p>You can also check out Brian&rsquo;s <a href="https://www.youtube.com/watch?v=zd-qV3XLR_k">Autonomy talk on 5/20/25</a>, <a href="https://youtu.be/tTy2Vhg2G-I">PhD Dissertation Defense on 4/26/22</a>, and <a href="https://www.youtube.com/watch?v=IFXlHAfr_v0">talk at Barnard on 12/14/21</a>, which provide overviews of several past research directions. For deeper background in robotics algorithms and mathematics, Russ Tedrake&rsquo;s <a href="https://courses.edx.org/courses/MITx/6.832x/3T2014/info">Underactuated Robotics</a> is an excellent resource. For GPU programming, <a href="https://enccs.github.io/gpu-programming/">this online course</a> provides a useful introduction.</p>
<p><em><strong>Questions not addressed above?</strong></em>
Email us at <strong><a href="mailto:plancher+A2R@dartmouth.edu">plancher+A2R@dartmouth.edu</a></strong>.</p>
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</description>
</item>
<item>
<title>Postdoctoral Researcher: GPU-Accelerated Uncertainty-Aware Optimal Control (Dartmouth x Toyota Research Institute)</title>
<link>https://a2r-lab.github.io/triuniversity3postdocadd/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://a2r-lab.github.io/triuniversity3postdocadd/</guid>
<description><p>The <a href="https://a2r-lab.org">Accessible and Accelerated Robotics (A2R) Lab</a> at Dartmouth College is hiring a Postdoctoral Researcher to work jointly with <a href="https://thomasjlew.github.io/">Thomas Lew</a> and the rest of the <a href="https://www.tri.global/our-work/human-interactive-driving">Human Interactive Driving</a> team at the Toyota Research Institute (TRI) on <strong>real-time, uncertainty-aware optimal control, targeting autonomous driving at the limits of handling, with validation on high-performance vehicles</strong>.</p>
<p><strong>Research topics may include (but are not limited to):</strong></p>
<ul>
<li>Novel (stochastic) optimal control algorithms and methods</li>
<li>Structure-exploiting numerical optimization solvers</li>
<li>Differentiable optimization</li>
<li>High-performance GPU implementations</li>
<li>Hybrid gradient-, sampling-, and learning-based methods</li>
<li>System integration and design for real-world deployments in dynamic environments</li>
</ul>
<p><strong>Requirements:</strong></p>
<ul>
<li>Ph.D. in CS, EE, Robotics, Controls, or related field</li>
<li>Strong foundations in optimal control/optimization and software engineering</li>
<li>GPU acceleration experience strongly preferred</li>
<li>Real-world robotics/autonomy experience and robot learning are a plus</li>
</ul>
<p><strong>Details:</strong> Position starts as early as April 2026; 1-year appointment with potential extension up to 3 years total. Based at Dartmouth (Hanover, NH) with hybrid collaboration with TRI (Los Altos, CA) and opportunities for trips to validate algorithms on high-performance vehicles on a racetrack in California.</p>
<p><strong>Apply:</strong> On <a href="https://apply.interfolio.com/179895">apply.interfolio.com/17989</a> with (i) cover letter, (ii) cv with contact information for 2–3 references, and (iii) a writing sample.</p>
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