A2R-Lab.github.io/index.xml at master · A2R-Lab/A2R-Lab.github.io · GitHub

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    <title>A²R Lab</title>
    <link>https://a2r-lab.github.io/</link>
    <description>Recent content on A²R Lab</description>
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    <item>
      <title>Publications</title>
      <link>https://a2r-lab.github.io/publication/</link>
      <pubDate>Tue, 07 Jul 2026 00:00:00 +0000</pubDate>

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      <description>&lt;p&gt;This list includes publications from the PI&amp;rsquo;s time at the Harvard &lt;a href=&#34;https://agile.seas.harvard.edu/&#34;&gt;Agile Robotics&lt;/a&gt; and &lt;a href=&#34;https://edge.seas.harvard.edu/&#34;&gt;Edge Computing&lt;/a&gt; Labs, as well as when the A2R Lab was affiliated with the &lt;a href=&#34;https://cs.barnard.edu/&#34;&gt;Department of Computer Science at Barnard College, Columbia University&lt;/a&gt;.&lt;/p&gt;
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      <title>Research Projects</title>
      <link>https://a2r-lab.github.io/projects/</link>
      <pubDate>Sat, 21 Aug 2021 00:00:00 +0000</pubDate>

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      <description>&lt;p&gt;Our lab&amp;rsquo;s core research question is: &lt;em&gt;how can we construct computational systems that enable robots to intelligently, flexibility, and reliably operate in the field?&lt;/em&gt; We seek to address this problem by &lt;strong&gt;developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, at all scales, through algorithm-hardware-software co-design&lt;/strong&gt;. 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.&lt;/p&gt;
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      <title></title>
      <link>https://a2r-lab.github.io/people/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>

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      <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>&lt;h3 id=&#34;paper-presentations-and-posters&#34;&gt;Paper Presentations and Posters&lt;/h3&gt;
&lt;p&gt;&lt;strong&gt;Tuesday 10:30am-12:00pm&lt;/strong&gt; &amp;ndash; Poster Session Tuesday 1:30pm-3:00pm&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://a2r-lab.org/publication/tinympc/&#34;&gt;&lt;strong&gt;TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers&lt;/strong&gt;&lt;/a&gt; (TuAA1-CC.1 - Award Session - CC-Main Hall)&lt;/li&gt;
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&lt;p&gt;&lt;strong&gt;Tuesday 1:30pm-3:00pm&lt;/strong&gt; &amp;ndash; Poster Session Tuesday 4:30pm-6:00pm&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://a2r-lab.org/publication/diffcompressdrl/&#34;&gt;&lt;strong&gt;Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning&lt;/strong&gt;&lt;/a&gt; (TuBT8-CC.7 - Reinforcement Learning I - CC-418)&lt;/li&gt;
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&lt;p&gt;&lt;strong&gt;Wednesday 10:30am-12:00pm&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://a2r-lab.org/publication/robotperf/&#34;&gt;&lt;strong&gt;RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance&lt;/strong&gt;&lt;/a&gt; (WeAT20-NT.8 - Performance Evaluation and Benchmarking - NT-G302)&lt;/li&gt;
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&lt;p&gt;&lt;strong&gt;Wednesday 1:30pm-3:00pm&lt;/strong&gt; &amp;ndash; Poster Session Wednesday 4:30pm-6:00pm&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://a2r-lab.org/publication/symstair/&#34;&gt;&lt;strong&gt;Symmetric Stair Preconditioning of Linear Systems for Parallel Trajectory Optimization&lt;/strong&gt;&lt;/a&gt; (WeBT18-AX.3 - Optimization and Optimal Control I - AX-206)&lt;/li&gt;
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&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://a2r-lab.org/publication/mpcgpu/&#34;&gt;&lt;strong&gt;MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU&lt;/strong&gt;&lt;/a&gt; (WeBT18-AX.4 - Optimization and Optimal Control I - AX-206)&lt;/li&gt;
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&lt;h3 id=&#34;workshop-panels-and-presentations&#34;&gt;Workshop Panels and Presentations&lt;/h3&gt;
&lt;p&gt;&lt;strong&gt;Friday 4:30-6:00pm&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://sites.google.com/site/adrienescandehomepage/ICRA2024ClimateChange&#34;&gt;&lt;strong&gt;Workshop on Robots and Roboticists in the Age of Climate Change&lt;/strong&gt; &amp;ndash; Panel Discussion&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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    <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>&lt;!-- raw HTML omitted --&gt;
&lt;ul&gt;
&lt;li&gt;Dartmouth Undergraduates, Masters, and PhD Students interested in joining the lab, or collaborating with us, should consider taking &lt;strong&gt;&lt;a href=&#34;https://brianplancher.com/courses/169.23-f25&#34;&gt;Parallel Optimization for Robotics&lt;/a&gt;&lt;/strong&gt; which will next be taught in the Winter 2026 Term.&lt;/li&gt;
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&lt;hr&gt;
&lt;p&gt;We are always looking to grow our team and welcome students at all levels: undergraduates, master’s students, PhD candidates, and postdoctoral researchers!&lt;/p&gt;
&lt;p&gt;Our research is highly interdisciplinary, spanning robotics, computer architecture, embedded systems, numerical optimization, and machine learning, with a unifying focus on developing &lt;strong&gt;edge computational systems through algorithm-hardware-software co-design&lt;/strong&gt;. Because of this breadth, it’s rare for incoming students to have experience across all of these areas, and as such, no prior background in robotics, parallel programming, or machine learning is required (though it’s a plus, especially as many projects involve deploying algorithms on edge robotic systems for real-world demonstrations). Instead, we look for strong foundations in &lt;strong&gt;applied mathematics, computer systems, and core software engineering skills&lt;/strong&gt; as these translate well across our projects and will set you up for success as you learn project-specific expertise.&lt;/p&gt;
&lt;p&gt;Most importantly, we value &lt;strong&gt;intellectual curiosity&lt;/strong&gt;, &lt;strong&gt;commitment&lt;/strong&gt;, &lt;strong&gt;clear communication&lt;/strong&gt;, &lt;strong&gt;creativity&lt;/strong&gt;, and the &lt;strong&gt;courage to learn something new&lt;/strong&gt;. Students eager to bridge multiple technical domains and engage deeply in collaborative research will thrive here. We also care about &lt;strong&gt;real-world impact&lt;/strong&gt; and encourage contributions to &lt;strong&gt;outreach and education&lt;/strong&gt;. An interest in connecting research to broader applications and sharing knowledge with the community is a strong asset.&lt;/p&gt;
&lt;p&gt;For Dartmouth undergraduates, master’s students, and PhD researchers, we strongly recommend you take  &lt;strong&gt;Parallel Optimization for Robotics&lt;/strong&gt;. It is excellent preparation for much of the lab’s research, and the course project is a great way to start exploring research.&lt;/p&gt;
&lt;p&gt;For Undergraduate and Masters part-time researchers during the academic semester, in order to ensure that you can have a meaningful research experience as a member of the lab we expect you to:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Commit at least 10 hours per week to your research work (similar to a course &amp;ndash; note that independent study research credit may be possible)&lt;/li&gt;
&lt;li&gt;Provide a (team) progress update every week either during lab meetings, at office hours, or through a scheduled meeting&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;There are also additional opportunities for full-time summer research experiences.&lt;/p&gt;
&lt;p&gt;Questions or interested in joining? Email us at: &lt;strong&gt;&lt;a href=&#34;mailto:plancher+A2R@dartmouth.edu&#34;&gt;plancher+A2R@dartmouth.edu&lt;/a&gt;&lt;/strong&gt;.&lt;/p&gt;
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&lt;h3 id=&#34;faqs&#34;&gt;FAQs:&lt;/h3&gt;
&lt;p&gt;&lt;em&gt;&lt;strong&gt;What kinds of research is the lab doing right now? Do you have any resources for getting started?&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;You can find descriptions of some of our current research directions on our &lt;a href=&#34;https://a2r-lab.github.io/projects/&#34;&gt;projects page&lt;/a&gt; as well as our recent publications on our &lt;a href=&#34;https://a2r-lab.github.io/publication/&#34;&gt;publications page&lt;/a&gt;. While most research projects will dive deep into specific areas of robotics and so there aren&amp;rsquo;t truly general resources that are applicale to all projects, as most (current) projects do explore hardware acceleration and optimal control to some degree, there are a few things you could do to prepare for research in the lab. First, as mentioned above, for Dartmouth students, considering taking Parallel Optimization for Robotics! Second, check out our PI&amp;rsquo;s &lt;a href=&#34;https://www.youtube.com/watch?v=zd-qV3XLR_k&#34;&gt;Autonomy talk on 5/20/25&lt;/a&gt;, &lt;a href=&#34;https://youtu.be/tTy2Vhg2G-I&#34;&gt;PhD Dissertation Defense on 4/26/22&lt;/a&gt; and &lt;a href=&#34;https://www.youtube.com/watch?v=IFXlHAfr_v0&#34;&gt;talk at Barnard on 12/14/21&lt;/a&gt;, which provide nice overviews of some projects we have done in the past (note that the Barnard talk is aimed at a more introductory audience). If you want to go deeper into the algorithms and math, Russ Tedrake&amp;rsquo;s &lt;a href=&#34;https://courses.edx.org/courses/MITx/6.832x/3T2014/info&#34;&gt;Underactuated Robotics&lt;/a&gt; course on edX covers many of those topics. If you want to learn more about GPU programming, &lt;a href=&#34;https://enccs.github.io/gpu-programming/&#34;&gt;this online course&lt;/a&gt; provides a nice introduction. Finally, we also explore how to increase the accessibility of cutting-edge computer science and robotics topics globally. This is best exemplified by our work with &lt;a href=&#34;https://tinymledu.org/&#34;&gt;TinyMLedu&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;&lt;em&gt;&lt;strong&gt;Is the position paid?&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;If you are doing research through a funded research program you will be funded and Dartmouth has many different programs that can provide funding for summer research &amp;ndash; for example, &lt;a href=&#34;https://graduate.dartmouth.edu/diversity/research-and-education/asure&#34;&gt;Dartmouth&amp;rsquo;s Academic Summer Undergraduate Research Experience (ASURE)&lt;/a&gt;!&lt;/p&gt;
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&lt;p&gt;We will also be constantly applying for grants to support additional student researchers, however, depending on timing, we may only be able to offer research as an independent study for credit. &lt;em&gt;Note: We do not expect you to be funded to join the lab! We can work to help you get funded AFTER you join! Also if you reach out ahead of time we can work together to get you funded BEFORE you start doing research.&lt;/em&gt;&lt;/p&gt;
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&lt;p&gt;&lt;em&gt;&lt;strong&gt;Why should I consider doing research?&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Research is a great way to learn more about a field of interest and develop both your technical and communication skills. Also, whether you are considering graduate school or industry positions, spending a few semesters doing research and publishing a peer-reviewed paper and/or presenting at regional and national conferences looks great on your resume!&lt;/p&gt;
</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>&lt;p&gt;The &lt;a href=&#34;https://a2r-lab.org&#34;&gt;Accessible and Accelerated Robotics (A2R) Lab&lt;/a&gt; at Dartmouth College is hiring a Postdoctoral Researcher to work jointly with &lt;a href=&#34;https://thomasjlew.github.io/&#34;&gt;Thomas Lew&lt;/a&gt; and the rest of the &lt;a href=&#34;https://www.tri.global/our-work/human-interactive-driving&#34;&gt;Human Interactive Driving&lt;/a&gt; team at the Toyota Research Institute (TRI) on &lt;strong&gt;real-time, uncertainty-aware optimal control, targeting autonomous driving at the limits of handling, with validation on high-performance vehicles&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Research topics may include (but are not limited to):&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Novel (stochastic) optimal control algorithms and methods&lt;/li&gt;
&lt;li&gt;Structure-exploiting numerical optimization solvers&lt;/li&gt;
&lt;li&gt;Differentiable optimization&lt;/li&gt;
&lt;li&gt;High-performance GPU implementations&lt;/li&gt;
&lt;li&gt;Hybrid gradient-, sampling-, and learning-based methods&lt;/li&gt;
&lt;li&gt;System integration and design for real-world deployments in dynamic environments&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Requirements:&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Ph.D. in CS, EE, Robotics, Controls, or related field&lt;/li&gt;
&lt;li&gt;Strong foundations in optimal control/optimization and software engineering&lt;/li&gt;
&lt;li&gt;GPU acceleration experience strongly preferred&lt;/li&gt;
&lt;li&gt;Real-world robotics/autonomy experience and robot learning are a plus&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Details:&lt;/strong&gt; 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.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Apply:&lt;/strong&gt; On &lt;a href=&#34;https://apply.interfolio.com/179895&#34;&gt;apply.interfolio.com/17989&lt;/a&gt; with (i) cover letter, (ii) cv with contact information for 2–3 references, and (iii) a writing sample.&lt;/p&gt;
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