Best Paper in Automation at IEEE ICRA 2024
Finalist for Best Conference Paper at IEEE ICRA 2024
Finalist for Best Student Paper at IEEE ICRA 2024
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A²R Lab
We are recruiting for PhD researchers for Fall 2026 starts. Please apply to the Dartmouth CS PhD program at the Guarini School of Graduate and Advanced Studies and mention your interest in our PI and lab to ensure full consideration. Applications are due December 15, 2025!
The Accessible and Accelerated Robotics Lab (A²R Lab)
We are recruiting for PhD researchers for Fall 2026 starts. Please apply to the Dartmouth CS PhD program at the Guarini School of Graduate and Advanced Studies and mention your interest in our PI and lab to ensure full consideration. Applications are due December 15, 2025!
The A²R Lab focuses on optimizing robotic systems at all scales by developing, optimizing, implementing, and evaluating next-generation algorithms and edge computational systems, through algorithm-hardware-software co-design (e.g., MPCGPU, GRiD, TinyMPC). As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning.
We also want 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 (e.g., Global TinyML Education, Parallel Optimization for Robotics).
Recent News
[10/24/25] We were awarded a Toyota Research Institute Univerisity 3.0 Grant to continue and expand our project on Real-Time Uncertainty-Aware Optimization for Intelligent Control!
[6/24/25] We had 2 papers accepted to IROS 2025: Robust and Efficient Embedded Convex Optimization through First-Order Adaptive Caching and Set Phasers to Stun: Beaming Power and Control to Mobile Robots with Laser Light! Also, our PI gave a talk and was on a panel at the RSS 2025 Workshop on Fast Motion Planning and Control in the Era of Parallelism! Recordings are available on the workshop website.
[5/20/25] Our PI gave an Autonomy Talk! You can find the recording at this link!
[5/11/25] We will be moving up to Dartmouth College and joining the Department Computer Science starting in Fall 2025!
[4/15/25] We were awarded a Toyota Research Institute Univerisity 2.0 Grant for Uncertainty-Aware Optimization for Intelligent Control!
[2/15/25] Registration is open for the Optimization for Robotics Summer School we are co-organizing July 14-18 at the University of Patras in Greece! Early-bird pricing is available until April 1st! Travel Awards and Fee Waivers are available due to support from an IEEE-RAS Technical Education Program Grant.
[12/23/24] Our workshop RoboARCH: Robotics Acceleration with Computing Hardware and Systems was accepted to the 2025 IEEE International Conference on Robotics and Automation! Consider submitting a poster (deadline April 1st)!
[9/6/24] We were awarded an NSF CSSI Grant to build an Accessible GPU-Accelerated Edge Optimal Control Library and Benchmarks!
[7/16/24] Our MPCGPU paper won the Best Poster Award at the IEEE-RAS TC on Model-Based Optimization for Robotics Virtual Poster Session!
[5/16/24] Our TinyMPC paper won the Best Paper in Automation and was a finalist for Best Conference Paper and Best Student Paper at the 2024 IEEE International Conference on Robotics and Automation (ICRA)!
Featured Publications
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
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.
MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU
Best Poster Award at the 2024 IEEE TC on Model Based Optimization for Robotics Virtual Poster Session
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.
GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
We introduce and release GRiD, an open-source, GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate nonlinear trajectory optimization through optimized code generation, GRiD provides as much as a 7.2x speedup over a state-of-the-art, multi-threaded CPU implementation and maintains as much as a 2.5x speedup when accounting for I/O overhead.