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<b> We are recruiting for PhD researchers for Fall 2028 starts through the <a href="https://graduate.dartmouth.edu/academics/programs/computer-science">Dartmouth CS PhD program</a>. Deadline: December 15th! <a href="/join/">More Info</a> </b>
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<p>The A²R Lab focuses on <strong>optimizing robotic systems at all scales</strong> by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, through algorithm-hardware-software co-design (e.g., <a href="/publication/mpcgpu/">MPCGPU</a>, <a href="/publication/grid/">GRiD</a>, <a href="/publication/tinympc/">TinyMPC</a>). As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning.</p>
<p>We also want to <strong>promote a responsible, sustainable, and accessible future for robotics and edge computing</strong>, 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 (e.g., <a href="/publication/scalingtinyml4d/">Global TinyML Education</a>, <a href="/publication/paroptrobcourse/">Parallel Optimization for Robotics</a>).</p>
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<p><strong>[9/4/26]</strong> <a href="/publication/mpcinjection/">MPC-Injection: Biasing Off-Policy Locomotion RL Toward Controller-Induced Behavior Basins</a> was accepted to CoRL 2026!</p>
<p><strong>[9/1/26]</strong> We were awarded 2 grants by the National Science Foundation (NSF)!</p>
<ul>
<li><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2621699">Scalable Cross-Layer Co-Design for Stable Mixed-Precision Acceleration</a> working with <a href="https://sites.bu.edu/sneuman/">Sabrina M. Neuman</a> and the <a href="https://sites.bu.edu/robomorphic/">Boston University Robomorphic Computing Laboratory</a></li>
<li><a href="https://www.nsf.gov/awardsearch/show-award?AWD_ID=2535096">Safe Multi-Task Learning and Control for Autonomy at the Edge</a> working with <a href="https://www.columbia.edu/~ja3451/">James Anderson</a> and his team in the <a href="https://www.ee.columbia.edu/">Columbia University Department of Electrical Engineering</a>.</li>
</ul>
<p><strong>[7/28/26]</strong> We had 2 more paper updates recently!</p>
<ul>
<li><a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a> was accepted to the Special Issue of the European Journal of Control (EJC) for the Best Papers from the 2026 European Control Conference (ECC)</li>
<li><a href="/publication/hjcdik/">HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent</a> was accepted to IROS 2026</li>
</ul>
<p><strong>[4/28/26]</strong> We had 4 more papers accepted recently!</p>
<ul>
<li>To RSS 2026: <a href="/publication/tinysdp/">TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics</a></li>
<li>To RA-L: <a href="/publication/phireman/">Robust Geospatial Coordination of Multi-Agent Communications Networks Under Attrition</a></li>
<li>To ECC 2026: <a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a></li>
<li>To IFAC WC 2026: <a href="/">Parallel Dynamic Programming for Conic Linear Quadratic Control</a></li>
</ul>
<p><strong>[1/31/26]</strong> We had 5 papers accepted to ICRA 2026 (including an RA-L transfer)!</p>
<ul>
<li><a href="/publication/gato/">GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control</a></li>
<li><a href="/publication/prrtc/">pRRTC: GPU-Parallel RRT-Connect for Fast, Consistent, and Low-Cost Motion Planning</a></li>
<li><a href="/publication/conictinympc/">Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC</a></li>
<li><a href="/publication/kaczmarz/">TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation</a></li>
<li><a href="/publication/roboprec/">RoboPrec: Enabling Reliable Embedded Computing for Robotics by Providing Accuracy Guarantees Across Mixed-Precision Datatypes (RA-L Transfer)</a></li>
</ul>
<p><strong>[10/24/25]</strong> We were awarded a <a href="https://www.tri.global/">Toyota Research Institute</a> Univerisity 3.0 Grant to continue and expand our project on Real-Time Uncertainty-Aware Optimization for Intelligent Control!</p>
<p><strong>[6/24/25]</strong> We had 2 papers accepted to IROS 2025: <a href="/publication/adaptivetinympc/">Robust and Efficient Embedded Convex Optimization through First-Order Adaptive Caching</a> and <a href="/publication/phaser/">Set Phasers to Stun: Beaming Power and Control to Mobile Robots with Laser Light</a>! Also, our PI gave a talk and was on a panel at the <a href="https://sites.google.com/rice.edu/parallelized-planning-control/">RSS 2025 Workshop on Fast Motion Planning and Control in the Era of Parallelism</a>! Recordings are available on the workshop website.</p>
<p><strong>[5/20/25]</strong> Our PI gave an <a href="https://gioele.science/autonomytalks.html">Autonomy Talk</a>! You can find the <a href="https://www.youtube.com/watch?v=zd-qV3XLR_k">recording at this link</a>!</p>
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<h1>Featured Publications</h1>
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<a href="/publication/tinympc/" itemprop="url">TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers</a>
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Best Paper in Automation at IEEE ICRA 2024
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Finalist for Best Conference Paper at IEEE ICRA 2024
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Finalist for Best Student Paper at IEEE ICRA 2024
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Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC both by benchmarking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 g quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance.
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<a href="https://xkhainguyen.github.io/" target="_blank">Khai Nguyen</a>
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<a href="https://samschoedel.com/" target="_blank">Sam Schoedel</a>
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<a href="https://www.linkedin.com/in/anoushka-alavilli-89586b178" target="_blank">Anoushka Alavilli</a>
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<a href="https://brianplancher.com" target="_blank">Brian Plancher</a>
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<a href="http://zacmanchester.github.io/" target="_blank">Zachary Manchester</a>
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2310.16985" target="_blank" rel="noopener">
PDF
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Cite
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/tinympc" target="_blank" rel="noopener">
Code
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="/files/TinyMPC_ICRA_Poster.pdf" target="_blank" rel="noopener">
Poster
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://www.youtube.com/watch?v=NKOrRyhcr6w" target="_blank" rel="noopener">
Video
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://doi.org/10.1109/ICRA57147.2024.10610987" target="_blank" rel="noopener">
DOI
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<a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://tinympc.org/" target="_blank" rel="noopener">
Website
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<a href="/publication/tinympc/">
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<a href="/publication/mpcgpu/" itemprop="url">MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU</a>
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Best Poster Award at the 2024 IEEE TC on Model Based Optimization for Robotics Virtual Poster Session
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We introduce MPCGPU, a GPU-accelerated, real-time NMPC solver that leverages an accelerated preconditioned conjugate gradient (PCG) linear system solver at its core. We show that MPCGPU increases the scalability and real-time performance of NMPC, solving larger problems, at faster rates. In particular, for tracking tasks using the Kuka IIWA manipulator, MPCGPU is able to scale to kilohertz control rates with trajectories as long as 512 knot points. This is driven by a custom PCG solver which outperforms state-of-the-art, CPU-based, linear system solvers by at least 10x for a majority of solves and 3.6x on average.
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<a href="https://www.linkedin.com/in/emre-adabag-223277174/" target="_blank">Emre Adabag</a>
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<a href="https://www.linkedin.com/in/miloniatal/" target="_blank">Miloni Atal</a>
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<a href="https://www.linkedin.com/in/gerard-william/" target="_blank">William Gerard</a>
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<a href="https://brianplancher.com" target="_blank">Brian Plancher</a>
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