The Accessible and Accelerated Robotics Lab (A²R Lab)
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).
GPU Linear Algebra Simple Subroutines: a header-only CUDA template library for block-local BLAS/LAPACK + robotics operators, tuned per-architecture with signed GPU test receipts
PCB design files, firmware, and FPGA solver code behind the paper "AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones"
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