Recent News

[6/24/25] I 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, I 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 of the workshop will be posted soon.

[5/20/25] I gave an Autonomy Talk! You can find the recording at this link!

[5/11/25] I will be moving to the Department of Computer Science at Dartmouth College starting in Fall 2025!

[4/15/25] I was 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 I am 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] My 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] I was awarded an NSF CSSI Grant to build an Accessible GPU-Accelerated Edge Optimal Control Library and Benchmarks!

[7/16/24] My lab’s MPCGPU paper won the Best Poster Award at the IEEE-RAS TC on Model-Based Optimization for Robotics Virtual Poster Session!

[5/16/24] My lab’s collaborative 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

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
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.
PDF Code Poster Video DOI Website

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.
PDF Code Poster Slides DOI

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.
PDF Code Poster Slides Video DOI Benchmark Experiments

Featured Courses

Instructor of Record
Many stages of state-of-the-art robotics pipelines rely on the solutions of underlying optimization algorithms. Unfortunately, many of these approaches rely on simplifications and conservative approximations in order to reduce their computational complexity and support online operation. At the same time, parallelism has been used to significantly increase the throughput of computationally expensive algorithms across the field of computer science. And, with the widespread adoption of parallel computing platforms such as GPUs, it is natural to consider whether these architectures can benefit robotics researchers interested in solving computationally constrained problems online. This course will provide students with an introduction to both parallel programming on GPUs as well as numerical optimization. It will then dive into the intersection of those fields through case studies of recent state-of-the-art research and culminate in a team-based final project.
Draft Syllabus

Co-Organizer
The overall goal of the Optimization for Robotics Summer School is to provide roboticists with a deeper understanding of the connections between seemingly disparate topics and to motivate the cross-pollination of ideas across subfields.
Website