Recent News

[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!

[6/7/24] We and our international collaborators were awarded an IEEE-RAS Technical Education Program Grant to run an “Optimization for Robotics Summer School” to be held in Summer 2025!

[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

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.
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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

Computer science (CS) and engineering research both have large and well documented gender diversity gaps. In fact, previous studies have reported that the overall CS Female Author Ratio (FAR) is only in the range of 16-26% and varies significantly between CS subfields ranging from as high as 42% in CS Education to as low as 8% in Theory and Algorithms. To understand the current state of gender diversity of robotics, we recently collected and analyzed the gender of paper authors from all IEEE RAS fully sponsored conferences (ARSO, CASE, HAPTICS, Humanoids, ICRA, ISAM, MEMS, RoboSoft, SIMPAR, SSRR) as well as IROS and RA-L from 2019-2021. Overall, we find that robotics has a long way to go to reach gender parity, with an overall FAR of only 11-12%. We hope this analysis helps the robotics community to continue to emphasize the importance of working to improve our diversity.
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